## 2016 |

Elvira, Victor; Miguez, Joaquin; Djuric, A Novel Algorithm for Adapting the Number of Particles in Particle Filtering (Inproceeding) 2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM), pp. 1–5, IEEE, Rio de Janeiro, 2016, ISBN: 978-1-5090-2103-1. @inproceedings{Elvira2016a, title = {A Novel Algorithm for Adapting the Number of Particles in Particle Filtering}, author = {Elvira, Victor and Miguez, Joaquin and Djuric, P.M.}, url = {http://ieeexplore.ieee.org/document/7569688/}, doi = {10.1109/SAM.2016.7569688}, isbn = {978-1-5090-2103-1}, year = {2016}, date = {2016-07-01}, booktitle = {2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)}, pages = {1--5}, publisher = {IEEE}, address = {Rio de Janeiro}, abstract = {In this paper, we propose a novel approach for assessing the convergence of particle filters in online manner. Particle filters sequentially approximate distributions of hidden states of state-space models. The approximations are random measures composed of weighted particles (i.e., samples of the state). A sufficiently large number of particles provides a good quality in the approximation but at the expense of increasing the computational load. We propose to adapt the number of particles in real time based on the convergence assessment of the particle filter. The proposed methodology is based on a model-independent theoretical analysis that is valid under mild assumptions. We present an algorithm that allows the practitioner to operate at a desirable operation point defined by a performance-complexity tradeoff. The algorithm has a small extra cost, and it shows good performance in our numerical simulations.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } In this paper, we propose a novel approach for assessing the convergence of particle filters in online manner. Particle filters sequentially approximate distributions of hidden states of state-space models. The approximations are random measures composed of weighted particles (i.e., samples of the state). A sufficiently large number of particles provides a good quality in the approximation but at the expense of increasing the computational load. We propose to adapt the number of particles in real time based on the convergence assessment of the particle filter. The proposed methodology is based on a model-independent theoretical analysis that is valid under mild assumptions. We present an algorithm that allows the practitioner to operate at a desirable operation point defined by a performance-complexity tradeoff. The algorithm has a small extra cost, and it shows good performance in our numerical simulations. |

Elvira, Victor; Miguez, Joaquin; Djuric, Online Adaptation of the Number of Particles of Sequential Monte Carlo Methods (Inproceeding) IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2016), Shanghai, 2016. (BibTeX) @inproceedings{Elvira2016, title = {Online Adaptation of the Number of Particles of Sequential Monte Carlo Methods}, author = {Elvira, Victor and Miguez, Joaquin and Djuric, P.M.}, year = {2016}, date = {2016-03-01}, booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2016)}, address = {Shanghai}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |

## 2015 |

Miguez, Joaquin; Mariño, Inés A Nonlinear Population Monte Carlo Scheme for Bayesian Parameter Estimation in a Stochastic Intercellular Network Model (Inproceeding) 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), pp. 497–500, IEEE, Cancun, 2015, ISBN: 978-1-4799-1963-5. @inproceedings{Miguez2015a, title = {A Nonlinear Population Monte Carlo Scheme for Bayesian Parameter Estimation in a Stochastic Intercellular Network Model}, author = {Miguez, Joaquin and Mariño, Inés}, url = {http://ieeexplore.ieee.org/document/7383845/}, doi = {10.1109/CAMSAP.2015.7383845}, isbn = {978-1-4799-1963-5}, year = {2015}, date = {2015-12-01}, booktitle = {2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)}, pages = {497--500}, publisher = {IEEE}, address = {Cancun}, abstract = {A nonlinear population Monte Carlo scheme for Bayesian parameter estimation in a stochastic intercellular network model}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } A nonlinear population Monte Carlo scheme for Bayesian parameter estimation in a stochastic intercellular network model |

Miguez, Joaquin; Crisan, Dan; Mariño, Inés Particle filtering for Bayesian parameter estimation in a high dimensional state space model (Inproceeding) 2015 23rd European Signal Processing Conference (EUSIPCO), pp. 1241–1245, IEEE, Nice, 2015, ISBN: 978-0-9928-6263-3. @inproceedings{Miguez2015, title = {Particle filtering for Bayesian parameter estimation in a high dimensional state space model}, author = {Miguez, Joaquin and Crisan, Dan and Mariño, Inés}, url = {http://ieeexplore.ieee.org/document/7362582/}, doi = {10.1109/EUSIPCO.2015.7362582}, isbn = {978-0-9928-6263-3}, year = {2015}, date = {2015-08-01}, booktitle = {2015 23rd European Signal Processing Conference (EUSIPCO)}, pages = {1241--1245}, publisher = {IEEE}, address = {Nice}, abstract = {Researchers in some of the most active fields of science, including, e.g., geophysics or systems biology, have to deal with very-large-scale stochastic dynamicmodels of realworld phenomena for which conventional prediction and estimation methods are not well suited. In this paper, we investigate the application of a novel nested particle filtering scheme for joint Bayesian parameter estimation and tracking of the dynamic variables in a high dimensional state space model –namely a stochastic version of the two-scale Lorenz 96 chaotic system, commonly used as a benchmark model in meteorology and climate science. We provide theoretical guarantees on the algorithm performance, including uniform convergence rates for the approximation of posterior probability density functions of the fixed model parameters.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Researchers in some of the most active fields of science, including, e.g., geophysics or systems biology, have to deal with very-large-scale stochastic dynamicmodels of realworld phenomena for which conventional prediction and estimation methods are not well suited. In this paper, we investigate the application of a novel nested particle filtering scheme for joint Bayesian parameter estimation and tracking of the dynamic variables in a high dimensional state space model –namely a stochastic version of the two-scale Lorenz 96 chaotic system, commonly used as a benchmark model in meteorology and climate science. We provide theoretical guarantees on the algorithm performance, including uniform convergence rates for the approximation of posterior probability density functions of the fixed model parameters. |

## 2014 |

Miguez, Joaquin On the uniform asymptotic convergence of a distributed particle filter (Inproceeding) 2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM), pp. 241–244, IEEE, A Coruña, 2014, ISBN: 978-1-4799-1481-4. @inproceedings{Miguez2014, title = {On the uniform asymptotic convergence of a distributed particle filter}, author = {Miguez, Joaquin}, url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6882385}, doi = {10.1109/SAM.2014.6882385}, isbn = {978-1-4799-1481-4}, year = {2014}, date = {2014-06-01}, booktitle = {2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM)}, pages = {241--244}, publisher = {IEEE}, address = {A Coruña}, abstract = {Distributed signal processing algorithms suitable for their implementation over wireless sensor networks (WSNs) and ad hoc networks with communications and computing capabilities have become a hot topic during the past years. One class of algorithms that have received special attention are particles filters. However, most distributed versions of this type of methods involve various heuristic or simplifying approximations and, as a consequence, classical convergence theorems for standard particle filters do not hold for their distributed counterparts. In this paper, we look into a distributed particle filter scheme that has been proposed for implementation in both parallel computing systems and WSNs, and prove that, under certain stability assumptions regarding the physical system of interest, its asymptotic convergence is guaranteed. Moreover, we show that convergence is attained uniformly over time. This means that approximation errors can be kept bounded for an arbitrarily long period of time without having to progressively increase the computational effort.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Distributed signal processing algorithms suitable for their implementation over wireless sensor networks (WSNs) and ad hoc networks with communications and computing capabilities have become a hot topic during the past years. One class of algorithms that have received special attention are particles filters. However, most distributed versions of this type of methods involve various heuristic or simplifying approximations and, as a consequence, classical convergence theorems for standard particle filters do not hold for their distributed counterparts. In this paper, we look into a distributed particle filter scheme that has been proposed for implementation in both parallel computing systems and WSNs, and prove that, under certain stability assumptions regarding the physical system of interest, its asymptotic convergence is guaranteed. Moreover, we show that convergence is attained uniformly over time. This means that approximation errors can be kept bounded for an arbitrarily long period of time without having to progressively increase the computational effort. |

Crisan, Dan; Miguez, Joaquin Nested Particle Filters for Sequential Parameter Estimation in Discrete-time State-space Models (Inproceeding) SIAM 2014 Conference on Uncertainty Quantification, Savannah, 2014. @inproceedings{Crisan2014b, title = {Nested Particle Filters for Sequential Parameter Estimation in Discrete-time State-space Models}, author = {Crisan, Dan and Miguez, Joaquin}, year = {2014}, date = {2014-03-01}, booktitle = {SIAM 2014 Conference on Uncertainty Quantification}, address = {Savannah}, abstract = {The problem of estimating the parameters of nonlinear, possibly non-Gaussian discrete-time state models has drawn considerable attention during the past few years, leading to the appearance of general methodologies (SMC2, particle MCMC, recursive ML) that have improved on earlier, simpler extensions of the standard particle filter. However, there is still a lack of recursive (online) methods that can provide a theoretically-grounded approximation of the joint posterior probability distribution of the parameters and the dynamic state variables of the model. In the talk, we will describe a two-layer particle filtering scheme that addresses this problem. Both a recursive algorithm, suitable for online implementation, and some results regarding its asymptotic convergence will be presented.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } The problem of estimating the parameters of nonlinear, possibly non-Gaussian discrete-time state models has drawn considerable attention during the past few years, leading to the appearance of general methodologies (SMC2, particle MCMC, recursive ML) that have improved on earlier, simpler extensions of the standard particle filter. However, there is still a lack of recursive (online) methods that can provide a theoretically-grounded approximation of the joint posterior probability distribution of the parameters and the dynamic state variables of the model. In the talk, we will describe a two-layer particle filtering scheme that addresses this problem. Both a recursive algorithm, suitable for online implementation, and some results regarding its asymptotic convergence will be presented. |

## 2013 |

Koblents, Eugenia; Miguez, Joaquin A Population Monte Carlo Scheme for Computational Inference in High Dimensional Spaces (Inproceeding) 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6318–6322, IEEE, Vancouver, 2013, ISSN: 1520-6149. @inproceedings{Koblents2013a, title = {A Population Monte Carlo Scheme for Computational Inference in High Dimensional Spaces}, author = {Koblents, Eugenia and Miguez, Joaquin}, url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6638881}, issn = {1520-6149}, year = {2013}, date = {2013-01-01}, booktitle = {2013 IEEE International Conference on Acoustics, Speech and Signal Processing}, pages = {6318--6322}, publisher = {IEEE}, address = {Vancouver}, abstract = {In this paper we address the Monte Carlo approximation of integrals with respect to probability distributions in high-dimensional spaces. In particular, we investigate the population Monte Carlo (PMC) scheme, which is based on an iterative importance sampling (IS) approach. Both IS and PMC suffer from the well known problem of degeneracy of the importance weights (IWs), which is closely related to the curse-of-dimensionality, and limits their applicability in large-scale practical problems. In this paper we investigate a novel PMC scheme that consists in performing nonlinear transformations of the IWs in order to smooth their variations and avoid degeneracy. We apply the modified IS scheme to the well-known mixture-PMC (MPMC) algorithm, which constructs the importance functions as mixtures of kernels. We present numerical results that show how the modified version of MPMC clearly outperforms the original scheme.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } In this paper we address the Monte Carlo approximation of integrals with respect to probability distributions in high-dimensional spaces. In particular, we investigate the population Monte Carlo (PMC) scheme, which is based on an iterative importance sampling (IS) approach. Both IS and PMC suffer from the well known problem of degeneracy of the importance weights (IWs), which is closely related to the curse-of-dimensionality, and limits their applicability in large-scale practical problems. In this paper we investigate a novel PMC scheme that consists in performing nonlinear transformations of the IWs in order to smooth their variations and avoid degeneracy. We apply the modified IS scheme to the well-known mixture-PMC (MPMC) algorithm, which constructs the importance functions as mixtures of kernels. We present numerical results that show how the modified version of MPMC clearly outperforms the original scheme. |

Koblents, Eugenia; Miguez, Joaquin Robust Mixture Population Monte Carlo Scheme with Adaptation of the Number of Components (Inproceeding) European Signal Processing Conference (EUSIPCO) 2013, Marrakech, 2013. @inproceedings{Koblents2013, title = {Robust Mixture Population Monte Carlo Scheme with Adaptation of the Number of Components}, author = {Koblents, Eugenia and Miguez, Joaquin}, url = {http://www.eusipco2013.org/}, year = {2013}, date = {2013-01-01}, booktitle = {European Signal Processing Conference (EUSIPCO) 2013}, address = {Marrakech}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |

Miguez, Joaquin; Koblents, Eugenia Particle Filtering with Transformed Weights (Inproceeding) 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), pp. 364–367, IEEE, St. Martin, 2013, ISBN: 978-1-4673-3146-3. @inproceedings{Miguez2013, title = {Particle Filtering with Transformed Weights}, author = {Miguez, Joaquin and Koblents, Eugenia}, url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6714083 http://www.deepdyve.com/lp/institute-of-electrical-and-electronics-engineers/particle-filtering-with-transformed-weights-gRZ3tDB6dc}, isbn = {978-1-4673-3146-3}, year = {2013}, date = {2013-01-01}, booktitle = {2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)}, pages = {364--367}, publisher = {IEEE}, address = {St. Martin}, abstract = {Particle filters are simulation-based algorithms for computational inference in dynamical systems that have become very popular over the years in many areas of science and engineering. They are derived from Bayes' theorem and the technique of importance sampling (IS), which entails the approximation of probability measures by way of weighted random samples in the space of interest. As a consequence, particle filters suffer from problems related to the degeneracy of these weights, a limitation shared with other IS-based methods. In practice, the weight degeneracy implies that in some scenarios (typically when the dimension of the state space is high or when the likelihood function of the system is sharp) classical particle filters become numerically unstable and fail to converge. In this paper we investigate the application of a recently proposed technique, termed nonlinear importance sampling (NIS), to the design of particle filters. We show how the standard particle filter can be easily modified to incorporate transformed weights computed according to the NIS scheme, then provide a concise proof of convergence of the resulting algorithm, and finally present computer simulation results to illustrate the potential improvement in performance that can be attained.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Particle filters are simulation-based algorithms for computational inference in dynamical systems that have become very popular over the years in many areas of science and engineering. They are derived from Bayes' theorem and the technique of importance sampling (IS), which entails the approximation of probability measures by way of weighted random samples in the space of interest. As a consequence, particle filters suffer from problems related to the degeneracy of these weights, a limitation shared with other IS-based methods. In practice, the weight degeneracy implies that in some scenarios (typically when the dimension of the state space is high or when the likelihood function of the system is sharp) classical particle filters become numerically unstable and fail to converge. In this paper we investigate the application of a recently proposed technique, termed nonlinear importance sampling (NIS), to the design of particle filters. We show how the standard particle filter can be easily modified to incorporate transformed weights computed according to the NIS scheme, then provide a concise proof of convergence of the resulting algorithm, and finally present computer simulation results to illustrate the potential improvement in performance that can be attained. |

## 2012 |

Koblents, Eugenia; Miguez, Joaquin Importance Sampling with Transformed Weights (Inproceeding) Data Assimilation Workshop, Oxford–Man Institute, Oxford, 2012. @inproceedings{Koblents2012, title = {Importance Sampling with Transformed Weights}, author = {Koblents, Eugenia and Miguez, Joaquin}, url = {http://www.oxford-man.ox.ac.uk/sites/default/files/events/Mon_24_JoaquinMiguez_06FINAL.pdf}, year = {2012}, date = {2012-01-01}, booktitle = {Data Assimilation Workshop, Oxford–Man Institute}, address = {Oxford}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |

## 2011 |

Achutegui, Katrin; Miguez, Joaquin A Parallel Resampling Scheme and its Application to Distributed Particle Filtering in Wireless Networks (Inproceeding) 2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), pp. 81–84, IEEE, San Juan, 2011, ISBN: 978-1-4577-2105-2. @inproceedings{Achutegui2011, title = {A Parallel Resampling Scheme and its Application to Distributed Particle Filtering in Wireless Networks}, author = {Achutegui, Katrin and Miguez, Joaquin}, url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6136051}, isbn = {978-1-4577-2105-2}, year = {2011}, date = {2011-01-01}, booktitle = {2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)}, pages = {81--84}, publisher = {IEEE}, address = {San Juan}, abstract = {We address the design of a particle filter (PF) that can be implemented in a distributed manner over a network of wireless sensor nodes, each of them collecting their own local data. This is a problem that has received considerable attention lately and several methods based on consensus, the transmission of likelihood information, the truncation and/or the quantization of data have been proposed. However, all existing schemes suffer from limitations related either to the amount of required communications among the nodes or the accuracy of the filter outputs. In this work we propose a novel distributed PF that is built around the distributed resampling with non-proportional allocation (DRNA) algorithm. This scheme guarantees the properness of the particle approximations produced by the filter and has been shown to be both efficient and accurate when compared with centralized PFs. The standard DRNA technique, however, places stringent demands on the communications among nodes that turn out impractical for a typical wireless sensor network (WSN). In this paper we investigate how to reduce this communication load by using (i) a random model for the spread of data over the WSN and (ii) methods that enable the out-of-sequence processing of sensor observations. A simple numerical illustration of the performance of the new algorithm compared with a centralized PF is provided.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } We address the design of a particle filter (PF) that can be implemented in a distributed manner over a network of wireless sensor nodes, each of them collecting their own local data. This is a problem that has received considerable attention lately and several methods based on consensus, the transmission of likelihood information, the truncation and/or the quantization of data have been proposed. However, all existing schemes suffer from limitations related either to the amount of required communications among the nodes or the accuracy of the filter outputs. In this work we propose a novel distributed PF that is built around the distributed resampling with non-proportional allocation (DRNA) algorithm. This scheme guarantees the properness of the particle approximations produced by the filter and has been shown to be both efficient and accurate when compared with centralized PFs. The standard DRNA technique, however, places stringent demands on the communications among nodes that turn out impractical for a typical wireless sensor network (WSN). In this paper we investigate how to reduce this communication load by using (i) a random model for the spread of data over the WSN and (ii) methods that enable the out-of-sequence processing of sensor observations. A simple numerical illustration of the performance of the new algorithm compared with a centralized PF is provided. |

Balasingam, Balakumar; Bolic, Miodrag; Djuric, Petar; Miguez, Joaquin Efficient Distributed Resampling for Particle Filters (Inproceeding) 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3772–3775, IEEE, Prague, 2011, ISSN: 1520-6149. @inproceedings{Balasingam2011, title = {Efficient Distributed Resampling for Particle Filters}, author = {Balasingam, Balakumar and Bolic, Miodrag and Djuric, Petar M. and Miguez, Joaquin}, url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5947172}, issn = {1520-6149}, year = {2011}, date = {2011-01-01}, booktitle = {2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, pages = {3772--3775}, publisher = {IEEE}, address = {Prague}, abstract = {In particle filtering, resampling is the only step that cannot be fully parallelized. Recently, we have proposed algorithms for distributed resampling implemented on architectures with concurrent processing elements (PEs). The objective of distributed resampling is to reduce the communication among the PEs while not compromising the performance of the particle filter. An additional objective for implementation is to reduce the communication among the PEs. In this paper, we report an improved version of the distributed resampling algorithm that optimally selects the particles for communication between the PEs of the distributed scheme. Computer simulations are provided that demonstrate the improved performance of the proposed algorithm.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } In particle filtering, resampling is the only step that cannot be fully parallelized. Recently, we have proposed algorithms for distributed resampling implemented on architectures with concurrent processing elements (PEs). The objective of distributed resampling is to reduce the communication among the PEs while not compromising the performance of the particle filter. An additional objective for implementation is to reduce the communication among the PEs. In this paper, we report an improved version of the distributed resampling algorithm that optimally selects the particles for communication between the PEs of the distributed scheme. Computer simulations are provided that demonstrate the improved performance of the proposed algorithm. |

Koblents, Eugenia; Miguez, Joaquin A Population Monte Carlo Method for Bayesian Inference and its Application to Stochastic Kinetic Models (Inproceeding) EUSIPCO 2011, Barcelona, 2011. @inproceedings{Koblents2011, title = {A Population Monte Carlo Method for Bayesian Inference and its Application to Stochastic Kinetic Models}, author = {Koblents, Eugenia and Miguez, Joaquin}, url = {http://www.eurasip.org/Proceedings/Eusipco/Eusipco2011/papers/1569427761.pdf}, year = {2011}, date = {2011-01-01}, booktitle = {EUSIPCO 2011}, address = {Barcelona}, abstract = {We introduce an extension of the population Monte Carlo (PMC) methodology to address the problem of Bayesian in- ference in high dimensional models. Specifically, we intro- duce a technique for the selection and update of importance functions based on the construction of Gaussian Bayesian networks. The structure of the latter graphical model en- ables a sequential sampling procedure that requires draw- ing only from unidimensional conditional distributions an d leads to very efficient PMC algorithms. In order to illus- trate the potential of the new technique we have consid- ered the estimation of rate parameters in stochastic kineti c models (SKMs). SKMs are multivariate systems that model molecular interactions in biological and chemical problem s. We present some numerical results based on a simple SKM known as predator-prey mode}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } We introduce an extension of the population Monte Carlo (PMC) methodology to address the problem of Bayesian in- ference in high dimensional models. Specifically, we intro- duce a technique for the selection and update of importance functions based on the construction of Gaussian Bayesian networks. The structure of the latter graphical model en- ables a sequential sampling procedure that requires draw- ing only from unidimensional conditional distributions an d leads to very efficient PMC algorithms. In order to illus- trate the potential of the new technique we have consid- ered the estimation of rate parameters in stochastic kineti c models (SKMs). SKMs are multivariate systems that model molecular interactions in biological and chemical problem s. We present some numerical results based on a simple SKM known as predator-prey mode |

Maiz, Cristina; Miguez, Joaquin On the Optimization of Transportation Routes with Multiple Destinations in Random Networks (Inproceeding) 2011 IEEE Statistical Signal Processing Workshop (SSP), pp. 349–352, IEEE, Nice, 2011, ISBN: 978-1-4577-0569-4. @inproceedings{Maiz2011, title = {On the Optimization of Transportation Routes with Multiple Destinations in Random Networks}, author = {Maiz, Cristina S. and Miguez, Joaquin}, url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5967701}, isbn = {978-1-4577-0569-4}, year = {2011}, date = {2011-01-01}, booktitle = {2011 IEEE Statistical Signal Processing Workshop (SSP)}, pages = {349--352}, publisher = {IEEE}, address = {Nice}, abstract = {Various practical problems in transportation research and routing in communication networks can be reduced to the computation of the best path that traverses a certain graph and visits a set of D specified destination nodes. Simple versions of this problem have received attention in the literature. Optimal solutions exist for the cases in which (a) D >; 1 and the graph is deterministic or (b) D = 1 and the graph is stochastic (and possibly time-dependent). Here, we address the general problem in which both D >; 1 and the costs of the edges in the graph are stochastic and time-varying. We tackle this complex global optimization problem by first converting it into an equivalent estimation problem and then computing a numerical solution using a sequential Monte Carlo algorithm. The advantage of the proposed technique over some standard methods (devised for graphs with time-invariant statistics) is illustrated by way of computer simulations.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Various practical problems in transportation research and routing in communication networks can be reduced to the computation of the best path that traverses a certain graph and visits a set of D specified destination nodes. Simple versions of this problem have received attention in the literature. Optimal solutions exist for the cases in which (a) D >; 1 and the graph is deterministic or (b) D = 1 and the graph is stochastic (and possibly time-dependent). Here, we address the general problem in which both D >; 1 and the costs of the edges in the graph are stochastic and time-varying. We tackle this complex global optimization problem by first converting it into an equivalent estimation problem and then computing a numerical solution using a sequential Monte Carlo algorithm. The advantage of the proposed technique over some standard methods (devised for graphs with time-invariant statistics) is illustrated by way of computer simulations. |

## 2010 |

Achutegui, Katrin; Rodas, Javier; Escudero, Carlos; Miguez, Joaquin A Model-Switching Sequential Monte Carlo Algorithm for Indoor Tracking with Experimental RSS Data (Inproceeding) 2010 International Conference on Indoor Positioning and Indoor Navigation, pp. 1–8, IEEE, Zurich, 2010, ISBN: 978-1-4244-5862-2. @inproceedings{Achutegui2010, title = {A Model-Switching Sequential Monte Carlo Algorithm for Indoor Tracking with Experimental RSS Data}, author = {Achutegui, Katrin and Rodas, Javier and Escudero, Carlos J. and Miguez, Joaquin}, url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5648053}, isbn = {978-1-4244-5862-2}, year = {2010}, date = {2010-01-01}, booktitle = {2010 International Conference on Indoor Positioning and Indoor Navigation}, pages = {1--8}, publisher = {IEEE}, address = {Zurich}, abstract = {In this paper we address the problem of indoor tracking using received signal strength (RSS) as position-dependent data. This type of measurements are very appealing because they can be easily obtained with a variety of (inexpensive) wireless technologies. However, the extraction of accurate location information from RSS in indoor scenarios is not an easy task. Due to the multipath propagation, it is hard to adequately model the correspondence between the received power and the transmitter-to-receiver distance. For that reason, we propose the use of a compound model that combines several sub-models, whose parameters are adjusted to different propagation environments. This methodology, called Interacting Multiple Models (IMM), has been used in the past either for modeling the motion of maneuvering targets or the relationship between the target position and the observations. Here, we extend its application to handle both types of uncertainty simultaneously and we refer to the resulting state-space model as a generalized IMM (GIMM) system. The flexibility of the GIMM approach is attained at the expense of an increase in the number of random processes that must be accurately tracked. To overcome this difficulty, we introduce a Rao-Blackwellized sequential Monte Carlo tracking algorithm that exhibits good performance both with synthetic and experimental data.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } In this paper we address the problem of indoor tracking using received signal strength (RSS) as position-dependent data. This type of measurements are very appealing because they can be easily obtained with a variety of (inexpensive) wireless technologies. However, the extraction of accurate location information from RSS in indoor scenarios is not an easy task. Due to the multipath propagation, it is hard to adequately model the correspondence between the received power and the transmitter-to-receiver distance. For that reason, we propose the use of a compound model that combines several sub-models, whose parameters are adjusted to different propagation environments. This methodology, called Interacting Multiple Models (IMM), has been used in the past either for modeling the motion of maneuvering targets or the relationship between the target position and the observations. Here, we extend its application to handle both types of uncertainty simultaneously and we refer to the resulting state-space model as a generalized IMM (GIMM) system. The flexibility of the GIMM approach is attained at the expense of an increase in the number of random processes that must be accurately tracked. To overcome this difficulty, we introduce a Rao-Blackwellized sequential Monte Carlo tracking algorithm that exhibits good performance both with synthetic and experimental data. |

Djuric, Petar; Closas, Pau; Bugallo, Monica; Miguez, Joaquin Evaluation of a Method's Robustness (Inproceeding) 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 3598–3601, IEEE, Dallas, 2010, ISSN: 1520-6149. @inproceedings{Djuric2010, title = {Evaluation of a Method's Robustness}, author = {Djuric, Petar M. and Closas, Pau and Bugallo, Monica F. and Miguez, Joaquin}, url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5495921}, issn = {1520-6149}, year = {2010}, date = {2010-01-01}, booktitle = {2010 IEEE International Conference on Acoustics, Speech and Signal Processing}, pages = {3598--3601}, publisher = {IEEE}, address = {Dallas}, abstract = {In signal processing, it is typical to develop or use a method based on a given model. In practice, however, we almost never know the actual model and we hope that the assumed model is in the neighborhood of the true one. If deviations exist, the method may be more or less sensitive to them. Therefore, it is important to know more about this sensitivity, or in other words, how robust the method is to model deviations. To that end, it is useful to have a metric that can quantify the robustness of the method. In this paper we propose a procedure for developing a variety of metrics for measuring robustness. They are based on a discrete random variable that is generated from observed data and data generated according to past data and the adopted model. This random variable is uniform if the model is correct. When the model deviates from the true one, the distribution of the random variable deviates from the uniform distribution. One can then employ measures for differences between distributions in order to quantify robustness. In this paper we describe the proposed methodology and demonstrate it with simulated data.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } In signal processing, it is typical to develop or use a method based on a given model. In practice, however, we almost never know the actual model and we hope that the assumed model is in the neighborhood of the true one. If deviations exist, the method may be more or less sensitive to them. Therefore, it is important to know more about this sensitivity, or in other words, how robust the method is to model deviations. To that end, it is useful to have a metric that can quantify the robustness of the method. In this paper we propose a procedure for developing a variety of metrics for measuring robustness. They are based on a discrete random variable that is generated from observed data and data generated according to past data and the adopted model. This random variable is uniform if the model is correct. When the model deviates from the true one, the distribution of the random variable deviates from the uniform distribution. One can then employ measures for differences between distributions in order to quantify robustness. In this paper we describe the proposed methodology and demonstrate it with simulated data. |

Helander,; Silén,; Miguez, Joaquin; Gabbouj, Maximum a Posteriori Voice Conversion Using Sequential Monte Carlo Methods (Inproceeding) Eleventh Annual Conference of the International Speech Communication Association (INTERSPEECH), Makuhari, Chiba, Japan, 2010. @inproceedings{Helander2010, title = {Maximum a Posteriori Voice Conversion Using Sequential Monte Carlo Methods}, author = {Helander, E. and Silén, H. and Miguez, Joaquin and Gabbouj, M.}, url = {http://www.isca-speech.org/archive/interspeech_2010/i10_1716.html}, year = {2010}, date = {2010-01-01}, booktitle = {Eleventh Annual Conference of the International Speech Communication Association (INTERSPEECH)}, address = {Makuhari, Chiba, Japan}, abstract = {Many voice conversion algorithms are based on frame-wise mapping from source features into target features. This ignores the inherent temporal continuity that is present in speech and can degrade the subjective quality. In this paper, we propose to optimize the speech feature sequence after a frame-based conversion algorithm has been applied. In particular, we select the sequence of speech features through the minimization of a cost function that involves both the conversion error and the smoothness of the sequence. The estimation problem is solved using sequential Monte Carlo methods. Both subjective and objective results show the effectiveness of the method.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Many voice conversion algorithms are based on frame-wise mapping from source features into target features. This ignores the inherent temporal continuity that is present in speech and can degrade the subjective quality. In this paper, we propose to optimize the speech feature sequence after a frame-based conversion algorithm has been applied. In particular, we select the sequence of speech features through the minimization of a cost function that involves both the conversion error and the smoothness of the sequence. The estimation problem is solved using sequential Monte Carlo methods. Both subjective and objective results show the effectiveness of the method. |

Martino, Luca; Miguez, Joaquin A Rejection Sampling Scheme for Posterior Probability Distributions via the Ratio-of-Uniforms Method (Inproceeding) 18th European Signal Processing Conference (EUSIPCO-2010), Aalborg, 2010. @inproceedings{Martino2010, title = {A Rejection Sampling Scheme for Posterior Probability Distributions via the Ratio-of-Uniforms Method}, author = {Martino, Luca and Miguez, Joaquin}, url = {http://www.academia.edu/2355638/A_rejection_sampling_scheme_for_posterior_probability_distributions_via_the_ratio-of-uniforms_method}, year = {2010}, date = {2010-01-01}, booktitle = {18th European Signal Processing Conference (EUSIPCO-2010)}, address = {Aalborg}, abstract = {Accept/reject sampling is a well-known method to generaterandom samples from arbitrary target probability distribu-tions. It demands the design of a suitable proposal probabil-ity density function (pdf) from which candidate samples canbe drawn. The main limitation to the use of RS is the needto ﬁnd an adequate upper bound for the ratio of the targetpdf over the proposal pdf from which the samples are gener-ated. There are no general methods to analytically ﬁnd thisbound, except when the target pdf is log-concave. In thispaper we introduce a novel procedure using the ratio of uni-forms method to eﬃciently perform rejection sampling fora large class of target densities. The candidate samples aregenerated using only two independent uniform random vari-ables. In order to illustrate the application of the proposedtechnique, we provide a numerical example}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Accept/reject sampling is a well-known method to generaterandom samples from arbitrary target probability distribu-tions. It demands the design of a suitable proposal probabil-ity density function (pdf) from which candidate samples canbe drawn. The main limitation to the use of RS is the needto ﬁnd an adequate upper bound for the ratio of the targetpdf over the proposal pdf from which the samples are gener-ated. There are no general methods to analytically ﬁnd thisbound, except when the target pdf is log-concave. In thispaper we introduce a novel procedure using the ratio of uni-forms method to eﬃciently perform rejection sampling fora large class of target densities. The candidate samples aregenerated using only two independent uniform random vari-ables. In order to illustrate the application of the proposedtechnique, we provide a numerical example |

Vazquez, Manuel; Miguez, Joaquin Adaptive MLSD for MIMO Transmission Systems with Unknown Subchannel Orders (Inproceeding) 2010 7th International Symposium on Wireless Communication Systems, pp. 451–455, IEEE, York, 2010, ISSN: 2154-0217. @inproceedings{Vazquez2010, title = {Adaptive MLSD for MIMO Transmission Systems with Unknown Subchannel Orders}, author = {Vazquez, Manuel A. and Miguez, Joaquin}, url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5624335}, issn = {2154-0217}, year = {2010}, date = {2010-01-01}, booktitle = {2010 7th International Symposium on Wireless Communication Systems}, pages = {451--455}, publisher = {IEEE}, address = {York}, abstract = {In the equalization of frequency-selective multiple-input multiple-output (MIMO) channels it is usually assumed that the length of the channel impulse response (CIR), also referred to as the channel order, is known. However, this is not true in most practical situations and, in order to avoid the serious performance degradation that occurs when the CIR length is underestimated, a channel with "more than enough" taps is usually considered. This very frequently leads to overestimating the channel order, which increases the computational complexity of any maximum likelihood sequence detection (MLSD) algorithm, while degrading its performance at the same time. The problem of estimating a single channel order for a time and frequency selective MIMO channel has recently been tackled. However, this is an idealized approach, since a MIMO channel comprises multiple subchannels (as many as the number of inputs times that of the outputs), each of them possibly with its own order. In this paper, we introduce an algorithm for MLSD that incorporates the full estimation of the MIMO CIR parameters, including one channel order per output. The proposed technique is based on the per survivor processing (PSP) methodology, it admits both blind and semiblind implementations, depending on the availability of pilot data, and it is designed to work with time-selective channels. Besides the analytical derivation of the algorithm, we provide computer simulation results that illustrate the effectiveness of the resulting receiver.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } In the equalization of frequency-selective multiple-input multiple-output (MIMO) channels it is usually assumed that the length of the channel impulse response (CIR), also referred to as the channel order, is known. However, this is not true in most practical situations and, in order to avoid the serious performance degradation that occurs when the CIR length is underestimated, a channel with "more than enough" taps is usually considered. This very frequently leads to overestimating the channel order, which increases the computational complexity of any maximum likelihood sequence detection (MLSD) algorithm, while degrading its performance at the same time. The problem of estimating a single channel order for a time and frequency selective MIMO channel has recently been tackled. However, this is an idealized approach, since a MIMO channel comprises multiple subchannels (as many as the number of inputs times that of the outputs), each of them possibly with its own order. In this paper, we introduce an algorithm for MLSD that incorporates the full estimation of the MIMO CIR parameters, including one channel order per output. The proposed technique is based on the per survivor processing (PSP) methodology, it admits both blind and semiblind implementations, depending on the availability of pilot data, and it is designed to work with time-selective channels. Besides the analytical derivation of the algorithm, we provide computer simulation results that illustrate the effectiveness of the resulting receiver. |

## 2009 |

Achutegui, Katrin; Martino, Luca; Rodas, Javier; Escudero, Carlos; Miguez, Joaquin A Multi-Model Particle Filtering Algorithm for Indoor Tracking of Mobile Terminals Using RSS Data (Inproceeding) 2009 IEEE International Conference on Control Applications, pp. 1702–1707, IEEE, Saint Petersburg, 2009, ISBN: 978-1-4244-4601-8. @inproceedings{Achutegui2009, title = {A Multi-Model Particle Filtering Algorithm for Indoor Tracking of Mobile Terminals Using RSS Data}, author = {Achutegui, Katrin and Martino, Luca and Rodas, Javier and Escudero, Carlos J. and Miguez, Joaquin}, url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5280960}, isbn = {978-1-4244-4601-8}, year = {2009}, date = {2009-01-01}, booktitle = {2009 IEEE International Conference on Control Applications}, pages = {1702--1707}, publisher = {IEEE}, address = {Saint Petersburg}, abstract = {In this paper we address the problem of indoor tracking using received signal strength (RSS) as a position-dependent data measurement. This type of measurements is very appealing because they can be easily obtained with a variety of wireless technologies which are relatively inexpensive. The extraction of accurate location information from RSS in indoor scenarios is not an easy task, though. Since RSS is highly influenced by multipath propagation, it turns out very hard to adequately model the correspondence between the received power and the transmitter-to-receiver distance. The measurement models proposed in the literature are site-specific and require a great deal of information regarding the structure of the building where the tracking will be performed and therefore are not useful for a general application. For that reason we propose the use of a compound model that combines several sub-models, whose parameters are adjusted to specific and different propagation environments. This methodology, is called interacting multiple models (IMM), has been used in the past for modeling the motion of maneuvering targets. Here, we extend its application to handle also the uncertainty in the RSS observations and we refer to the resulting state-space model as a generalized IMM (GIMM) system. The flexibility of the GIMM approach is attained at the expense of an increase in the number of random processes that must be accurately tracked. To overcome this difficulty, we introduce a Rao-Blackwellized sequential Monte Carlo tracking algorithm that exhibits good performance both with synthetic and experimental data.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } In this paper we address the problem of indoor tracking using received signal strength (RSS) as a position-dependent data measurement. This type of measurements is very appealing because they can be easily obtained with a variety of wireless technologies which are relatively inexpensive. The extraction of accurate location information from RSS in indoor scenarios is not an easy task, though. Since RSS is highly influenced by multipath propagation, it turns out very hard to adequately model the correspondence between the received power and the transmitter-to-receiver distance. The measurement models proposed in the literature are site-specific and require a great deal of information regarding the structure of the building where the tracking will be performed and therefore are not useful for a general application. For that reason we propose the use of a compound model that combines several sub-models, whose parameters are adjusted to specific and different propagation environments. This methodology, is called interacting multiple models (IMM), has been used in the past for modeling the motion of maneuvering targets. Here, we extend its application to handle also the uncertainty in the RSS observations and we refer to the resulting state-space model as a generalized IMM (GIMM) system. The flexibility of the GIMM approach is attained at the expense of an increase in the number of random processes that must be accurately tracked. To overcome this difficulty, we introduce a Rao-Blackwellized sequential Monte Carlo tracking algorithm that exhibits good performance both with synthetic and experimental data. |

Bugallo, Monica; Maiz, Cristina; Miguez, Joaquin; Djuric, Petar Cost-Reference Particle Filters and Fusion of Information (Inproceeding) 2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, pp. 286–291, IEEE, Marco Island, FL, 2009. @inproceedings{Bugallo2009, title = {Cost-Reference Particle Filters and Fusion of Information}, author = {Bugallo, Monica F. and Maiz, Cristina S. and Miguez, Joaquin and Djuric, Petar M.}, url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4785936}, year = {2009}, date = {2009-01-01}, booktitle = {2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop}, pages = {286--291}, publisher = {IEEE}, address = {Marco Island, FL}, abstract = {Cost-reference particle filtering is a methodology for tracking unknowns in a system without reliance on probabilistic information about the noises in the system. The methodology is based on analogous principles as the ones of standard particle filtering. Unlike the random measures of standard particle filters that are composed of particles and weights, the random measures of cost-reference particle filters contain particles and user-defined costs. In this paper, we discuss a few scenarios where we need to meld random measures of two or more cost-reference particle filters. The objective is to obtain a fused random measure that combines the information from the individual cost-reference particle filters.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Cost-reference particle filtering is a methodology for tracking unknowns in a system without reliance on probabilistic information about the noises in the system. The methodology is based on analogous principles as the ones of standard particle filtering. Unlike the random measures of standard particle filters that are composed of particles and weights, the random measures of cost-reference particle filters contain particles and user-defined costs. In this paper, we discuss a few scenarios where we need to meld random measures of two or more cost-reference particle filters. The objective is to obtain a fused random measure that combines the information from the individual cost-reference particle filters. |

Djuric, Petar; Bugallo, Monica; Closas, Pau; Miguez, Joaquin Measuring the Robustness of Sequential Methods (Inproceeding) 2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, pp. 29–32, IEEE, Aruba, Dutch Antilles, 2009, ISBN: 978-1-4244-5179-1. @inproceedings{Djuric2009a, title = {Measuring the Robustness of Sequential Methods}, author = {Djuric, Petar M. and Bugallo, Monica F. and Closas, Pau and Miguez, Joaquin}, url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5413275}, isbn = {978-1-4244-5179-1}, year = {2009}, date = {2009-01-01}, booktitle = {2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop}, pages = {29--32}, publisher = {IEEE}, address = {Aruba, Dutch Antilles}, abstract = {Whenever we apply methods for processing data, we make a number of model assumptions. In reality, these assumptions are not always correct. Robust methods can withstand model inaccuracies, that is, despite some incorrect assumptions they can still produce good results. We often want to know how robust employed methods are. To that end we need to have a yardstick for measuring robustness. In this paper, we propose an approach for constructing such metrics for sequential methods. These metrics are derived from the Kolmogorov-Smirnov distance between the cumulative distribution functions of the actual observations and the ones based on the assumed model. The use of the proposed metrics is demonstrated with simulation examples.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Whenever we apply methods for processing data, we make a number of model assumptions. In reality, these assumptions are not always correct. Robust methods can withstand model inaccuracies, that is, despite some incorrect assumptions they can still produce good results. We often want to know how robust employed methods are. To that end we need to have a yardstick for measuring robustness. In this paper, we propose an approach for constructing such metrics for sequential methods. These metrics are derived from the Kolmogorov-Smirnov distance between the cumulative distribution functions of the actual observations and the ones based on the assumed model. The use of the proposed metrics is demonstrated with simulation examples. |

Djuric, Petar; Miguez, Joaquin Model Assessment with Kolmogorov-Smirnov Statistics (Inproceeding) 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 2973–2976, IEEE, Taipei, 2009, ISSN: 1520-6149. @inproceedings{Djuric2009, title = {Model Assessment with Kolmogorov-Smirnov Statistics}, author = {Djuric, Petar M. and Miguez, Joaquin}, url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4960248}, issn = {1520-6149}, year = {2009}, date = {2009-01-01}, booktitle = {2009 IEEE International Conference on Acoustics, Speech and Signal Processing}, pages = {2973--2976}, publisher = {IEEE}, address = {Taipei}, abstract = {One of the most basic problems in science and engineering is the assessment of a considered model. The model should describe a set of observed data and the objective is to find ways of deciding if the model should be rejected. It seems that this is an ill-conditioned problem because we have to test the model against all the possible alternative models. In this paper we use the Kolmogorov-Smirnov statistic to develop a test that shows if the model should be kept or it should be rejected. We explain how this testing can be implemented in the context of particle filtering. We demonstrate the performance of the proposed method by computer simulations.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } One of the most basic problems in science and engineering is the assessment of a considered model. The model should describe a set of observed data and the objective is to find ways of deciding if the model should be rejected. It seems that this is an ill-conditioned problem because we have to test the model against all the possible alternative models. In this paper we use the Kolmogorov-Smirnov statistic to develop a test that shows if the model should be kept or it should be rejected. We explain how this testing can be implemented in the context of particle filtering. We demonstrate the performance of the proposed method by computer simulations. |

Maiz, Cristina; Miguez, Joaquin; Djuric, Petar Particle Filtering in the Presence of Outliers (Inproceeding) 2009 IEEE/SP 15th Workshop on Statistical Signal Processing, pp. 33–36, IEEE, Cardiff, 2009, ISBN: 978-1-4244-2709-3. @inproceedings{Maiz2009, title = {Particle Filtering in the Presence of Outliers}, author = {Maiz, Cristina S. and Miguez, Joaquin and Djuric, Petar M.}, url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5278645}, isbn = {978-1-4244-2709-3}, year = {2009}, date = {2009-01-01}, booktitle = {2009 IEEE/SP 15th Workshop on Statistical Signal Processing}, pages = {33--36}, publisher = {IEEE}, address = {Cardiff}, abstract = {Particle filters have become very popular signal processing tools for problems that involve nonlinear tracking of an unobserved signal of interest given a series of related observations. In this paper we propose a new scheme for particle filtering when the observed data are possibly contaminated with outliers. An outlier is an observation that has been generated by some (unknown) mechanism different from the assumed model of the data. Therefore, when handled in the same way as regular observations, outliers may drastically degrade the performance of the particle filter. To address this problem, we introduce an auxiliary particle filtering scheme that incorporates an outlier detection step. We propose to implement it by means of a test involving statistics of the predictive distributions of the observations. Specifically, we investigate the use of a proposed statistic called spatial depth that can easily be applied to multidimensional random variates. The performance of the resulting algorithm is assessed by computer simulations of target tracking based on signal-power observations.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Particle filters have become very popular signal processing tools for problems that involve nonlinear tracking of an unobserved signal of interest given a series of related observations. In this paper we propose a new scheme for particle filtering when the observed data are possibly contaminated with outliers. An outlier is an observation that has been generated by some (unknown) mechanism different from the assumed model of the data. Therefore, when handled in the same way as regular observations, outliers may drastically degrade the performance of the particle filter. To address this problem, we introduce an auxiliary particle filtering scheme that incorporates an outlier detection step. We propose to implement it by means of a test involving statistics of the predictive distributions of the observations. Specifically, we investigate the use of a proposed statistic called spatial depth that can easily be applied to multidimensional random variates. The performance of the resulting algorithm is assessed by computer simulations of target tracking based on signal-power observations. |

Martino, Luca; Miguez, Joaquin An Adaptive Accept/Reject Sampling Algorithm for Posterior Probability Distributions (Inproceeding) 2009 IEEE/SP 15th Workshop on Statistical Signal Processing, pp. 45–48, IEEE, Cardiff, 2009, ISBN: 978-1-4244-2709-3. @inproceedings{Martino2009b, title = {An Adaptive Accept/Reject Sampling Algorithm for Posterior Probability Distributions}, author = {Martino, Luca and Miguez, Joaquin}, url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5278644}, isbn = {978-1-4244-2709-3}, year = {2009}, date = {2009-01-01}, booktitle = {2009 IEEE/SP 15th Workshop on Statistical Signal Processing}, pages = {45--48}, publisher = {IEEE}, address = {Cardiff}, abstract = {Accept/reject sampling is a well-known method to generate random samples from arbitrary target probability distributions. It demands the design of a suitable proposal probability density function (pdf) from which candidate samples can be drawn. These samples are either accepted or rejected depending on a test involving the ratio of the target and proposal densities. In this paper we introduce an adaptive method to build a sequence of proposal pdf's that approximate the target density and hence can ensure a high acceptance rate. In order to illustrate the application of the method we design an accept/reject particle filter and then assess its performance and sampling efficiency numerically, by means of computer simulations.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Accept/reject sampling is a well-known method to generate random samples from arbitrary target probability distributions. It demands the design of a suitable proposal probability density function (pdf) from which candidate samples can be drawn. These samples are either accepted or rejected depending on a test involving the ratio of the target and proposal densities. In this paper we introduce an adaptive method to build a sequence of proposal pdf's that approximate the target density and hence can ensure a high acceptance rate. In order to illustrate the application of the method we design an accept/reject particle filter and then assess its performance and sampling efficiency numerically, by means of computer simulations. |

Martino, Luca; Miguez, Joaquin New Accept/Reject Methods for Independent Sampling from Posterior Probability Distributions (Inproceeding) 17th European Signal Processing Conference (EUSIPCO 2009), Glasgow, 2009. @inproceedings{Martino2009a, title = {New Accept/Reject Methods for Independent Sampling from Posterior Probability Distributions}, author = {Martino, Luca and Miguez, Joaquin}, url = {http://www.academia.edu/2355641/NEW_ACCEPT_REJECT_METHODS_FOR_INDEPENDENT_SAMPLING_FROM_POSTERIOR_PROBABILITY_DISTRIBUTIONS}, year = {2009}, date = {2009-01-01}, booktitle = {17th European Signal Processing Conference (EUSIPCO 2009)}, address = {Glasgow}, abstract = {Rejection sampling (RS) is a well-known method to generate(pseudo-)random samples from arbitrary probability distributionsthat enjoys important applications, either by itself or as a tool inmore sophisticated Monte Carlo techniques. Unfortunately, the useof RS techniques demands the calculation of tight upper bounds forthe ratio of the target probability density function (pdf) over theproposal density from which candidate samples are drawn. Exceptfor the class of log-concave target pdf’s, for which an efﬁcientalgorithm exists, there are no general methods to analyticallydetermine this bound, which has to be derived from scratch foreach speciﬁc case. In this paper, we tackle the general problemof applying RS to draw from an arbitrary posterior pdf using theprior density as a proposal function. This is a scenario that appearsfrequently in Bayesian signal processing methods. We derive ageneral geometric procedure for the calculation of upper boundsthat can be used with a broad class of target pdf’s, includingscenarios with correlated observations, multimodal and/or mixturemeasurement noises. We provide some simple numerical examplesto illustrate the application of the proposed techniques}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Rejection sampling (RS) is a well-known method to generate(pseudo-)random samples from arbitrary probability distributionsthat enjoys important applications, either by itself or as a tool inmore sophisticated Monte Carlo techniques. Unfortunately, the useof RS techniques demands the calculation of tight upper bounds forthe ratio of the target probability density function (pdf) over theproposal density from which candidate samples are drawn. Exceptfor the class of log-concave target pdf’s, for which an efﬁcientalgorithm exists, there are no general methods to analyticallydetermine this bound, which has to be derived from scratch foreach speciﬁc case. In this paper, we tackle the general problemof applying RS to draw from an arbitrary posterior pdf using theprior density as a proposal function. This is a scenario that appearsfrequently in Bayesian signal processing methods. We derive ageneral geometric procedure for the calculation of upper boundsthat can be used with a broad class of target pdf’s, includingscenarios with correlated observations, multimodal and/or mixturemeasurement noises. We provide some simple numerical examplesto illustrate the application of the proposed techniques |

Martino, Luca; Miguez, Joaquin A Novel Rejection Sampling Scheme for Posterior Probability Distributions (Inproceeding) 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 2921–2924, IEEE, Taipei, 2009, ISSN: 1520-6149. @inproceedings{Martino2009, title = {A Novel Rejection Sampling Scheme for Posterior Probability Distributions}, author = {Martino, Luca and Miguez, Joaquin}, url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4960235}, issn = {1520-6149}, year = {2009}, date = {2009-01-01}, booktitle = {2009 IEEE International Conference on Acoustics, Speech and Signal Processing}, pages = {2921--2924}, publisher = {IEEE}, address = {Taipei}, abstract = {Rejection sampling (RS) is a well-known method to draw from arbitrary target probability distributions, which has important applications by itself or as a building block for more sophisticated Monte Carlo techniques. The main limitation to the use of RS is the need to find an adequate upper bound for the ratio of the target probability density function (pdf) over the proposal pdf from which the samples are generated. There are no general methods to analytically find this bound, except in the particular case in which the target pdf is log-concave. In this paper we adopt a Bayesian view of the problem and propose a general RS scheme to draw from the posterior pdf of a signal of interest using its prior density as a proposal function. The method enables the analytical calculation of the bound and can be applied to a large class of target densities. We illustrate its use with a simple numerical example.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Rejection sampling (RS) is a well-known method to draw from arbitrary target probability distributions, which has important applications by itself or as a building block for more sophisticated Monte Carlo techniques. The main limitation to the use of RS is the need to find an adequate upper bound for the ratio of the target probability density function (pdf) over the proposal pdf from which the samples are generated. There are no general methods to analytically find this bound, except in the particular case in which the target pdf is log-concave. In this paper we adopt a Bayesian view of the problem and propose a general RS scheme to draw from the posterior pdf of a signal of interest using its prior density as a proposal function. The method enables the analytical calculation of the bound and can be applied to a large class of target densities. We illustrate its use with a simple numerical example. |

Miguez, Joaquin; Maiz, Cristina; Djuric, Petar; Crisan, Dan Sequential Monte Carlo Optimization Using Artificial State-Space Models (Inproceeding) 2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, pp. 268–273, IEEE, Marco Island, FL, 2009. @inproceedings{Miguez2009, title = {Sequential Monte Carlo Optimization Using Artificial State-Space Models}, author = {Miguez, Joaquin and Maiz, Cristina S. and Djuric, Petar M. and Crisan, Dan}, url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4785933}, year = {2009}, date = {2009-01-01}, booktitle = {2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop}, pages = {268--273}, publisher = {IEEE}, address = {Marco Island, FL}, abstract = {We introduce a method for sequential minimization of a certain class of (possibly non-convex) cost functions with respect to a high dimensional signal of interest. The proposed approach involves the transformation of the optimization problem into one of estimation in a discrete-time dynamical system. In particular, we describe a methodology for constructing an artificial state-space model which has the signal of interest as its unobserved dynamic state. The model is "adapted" to the cost function in the sense that the maximum a posteriori (MAP) estimate of the system state is also a global minimizer of the cost function. The advantage of the estimation framework is that we can draw from a pool of sequential Monte Carlo methods, for particle approximation of probability measures in dynamic systems, that enable the numerical computation of MAP estimates. We provide examples of how to apply the proposed methodology, including some illustrative simulation results.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } We introduce a method for sequential minimization of a certain class of (possibly non-convex) cost functions with respect to a high dimensional signal of interest. The proposed approach involves the transformation of the optimization problem into one of estimation in a discrete-time dynamical system. In particular, we describe a methodology for constructing an artificial state-space model which has the signal of interest as its unobserved dynamic state. The model is "adapted" to the cost function in the sense that the maximum a posteriori (MAP) estimate of the system state is also a global minimizer of the cost function. The advantage of the estimation framework is that we can draw from a pool of sequential Monte Carlo methods, for particle approximation of probability measures in dynamic systems, that enable the numerical computation of MAP estimates. We provide examples of how to apply the proposed methodology, including some illustrative simulation results. |

## 2008 |

Miguez, Joaquin Analysis of a Sequential Monte Carlo Optimization Methodology (Inproceeding) 16th European Signal Processing Conference (EUSIPCO 2008, Lausanne, 2008. @inproceedings{Miguez2008, title = {Analysis of a Sequential Monte Carlo Optimization Methodology}, author = {Miguez, Joaquin}, url = {http://www.eurasip.org/Proceedings/Eusipco/Eusipco2008/papers/1569105254.pdf}, year = {2008}, date = {2008-01-01}, booktitle = {16th European Signal Processing Conference (EUSIPCO 2008}, address = {Lausanne}, abstract = {We investigate a family of stochastic exploration methods that has been recently proposed to carry out estimation and prediction in discrete-time random dynamical systems. The key of the novel approach is to identify a cost function whose minima provide valid estimates of the system state at successive time instants. This function is recursively optimized using a sequential Monte Carlo minimization (SMCM) procedure which is similar to standard particle filtering algorithms but does not require a explicit probabilistic model to be imposed on the system. In this paper, we analyze the asymptotic convergence of SMCM methods and show that a properly designed algorithm produces a sequence of system-state estimates with individually minimal contributions to the cost function. We apply the SMCM method to a target tracking problem in order to illustrate how convergence is achieved in the way predicted by the theory.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } We investigate a family of stochastic exploration methods that has been recently proposed to carry out estimation and prediction in discrete-time random dynamical systems. The key of the novel approach is to identify a cost function whose minima provide valid estimates of the system state at successive time instants. This function is recursively optimized using a sequential Monte Carlo minimization (SMCM) procedure which is similar to standard particle filtering algorithms but does not require a explicit probabilistic model to be imposed on the system. In this paper, we analyze the asymptotic convergence of SMCM methods and show that a properly designed algorithm produces a sequence of system-state estimates with individually minimal contributions to the cost function. We apply the SMCM method to a target tracking problem in order to illustrate how convergence is achieved in the way predicted by the theory. |

Vazquez, Manuel; Miguez, Joaquin A Per-Survivor Processing Algorithm for Maximum Likelihood Equalization of MIMO Channels with Unknown Order (Inproceeding) 2008 International ITG Workshop on Smart Antennas, pp. 387–391, IEEE, Vienna, 2008, ISBN: 978-1-4244-1756-8. @inproceedings{Vazquez2008a, title = {A Per-Survivor Processing Algorithm for Maximum Likelihood Equalization of MIMO Channels with Unknown Order}, author = {Vazquez, Manuel A. and Miguez, Joaquin}, url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4475587}, isbn = {978-1-4244-1756-8}, year = {2008}, date = {2008-01-01}, booktitle = {2008 International ITG Workshop on Smart Antennas}, pages = {387--391}, publisher = {IEEE}, address = {Vienna}, abstract = {In the equalization of frequency-selective multiple-input multiple-output (MIMO) channels it is usually assumed that the length of the channel impulse response (CIR), also referred to as the channel order, is known. However, this is not true in most practical situations and, in order to avoid the serious performance degradation that occurs when the CIR length is underestimated, a channel with "more than enough" taps is usually considered. This possibly means overestimating the channel order, and is not desirable since the computational complexity of maximum likelihood sequence detection (MLSD) in frequency-selective channels grows exponentially with the channel order. In addition to that, the higher the channel order considered, the more the number of channel coefficients that need to be estimated from the same set of observations. In this paper, we introduce an algorithm for MLSD that incorporates the full estimation of the MIMO CIR parameters, including its order. The proposed technique is based on the per survivor processing (PSP) methodology, it admits both blind and semiblind implementations, depending on the availability of pilot data, and is designed to work with time-selective channels. Besides the analytical derivation of the algorithm, we provide computer simulation results that illustrate the effectiveness of the resulting receiver.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } In the equalization of frequency-selective multiple-input multiple-output (MIMO) channels it is usually assumed that the length of the channel impulse response (CIR), also referred to as the channel order, is known. However, this is not true in most practical situations and, in order to avoid the serious performance degradation that occurs when the CIR length is underestimated, a channel with "more than enough" taps is usually considered. This possibly means overestimating the channel order, and is not desirable since the computational complexity of maximum likelihood sequence detection (MLSD) in frequency-selective channels grows exponentially with the channel order. In addition to that, the higher the channel order considered, the more the number of channel coefficients that need to be estimated from the same set of observations. In this paper, we introduce an algorithm for MLSD that incorporates the full estimation of the MIMO CIR parameters, including its order. The proposed technique is based on the per survivor processing (PSP) methodology, it admits both blind and semiblind implementations, depending on the availability of pilot data, and is designed to work with time-selective channels. Besides the analytical derivation of the algorithm, we provide computer simulation results that illustrate the effectiveness of the resulting receiver. |

Vazquez, Manuel; Miguez, Joaquin A Per-Survivor Processing Algorithm for Maximum Likelihood Equalization of MIMO Channels with Unknown Order (Inproceeding) 2008 International ITG Workshop on Smart Antennas, pp. 387–391, IEEE, Vienna, 2008, ISBN: 978-1-4244-1756-8. @inproceedings{Vazquez2008, title = {A Per-Survivor Processing Algorithm for Maximum Likelihood Equalization of MIMO Channels with Unknown Order}, author = {Vazquez, Manuel A. and Miguez, Joaquin}, url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=4475587}, isbn = {978-1-4244-1756-8}, year = {2008}, date = {2008-01-01}, booktitle = {2008 International ITG Workshop on Smart Antennas}, pages = {387--391}, publisher = {IEEE}, address = {Vienna}, abstract = {In the equalization of frequency-selective multiple-input multiple-output (MIMO) channels it is usually assumed that the length of the channel impulse response (CIR), also referred to as the channel order, is known. However, this is not true in most practical situations and, in order to avoid the serious performance degradation that occurs when the CIR length is underestimated, a channel with "more than enough" taps is usually considered. This possibly means overestimating the channel order, and is not desirable since the computational complexity of maximum likelihood sequence detection (MLSD) in frequency-selective channels grows exponentially with the channel order. In addition to that, the higher the channel order considered, the more the number of channel coefficients that need to be estimated from the same set of observations. In this paper, we introduce an algorithm for MLSD that incorporates the full estimation of the MIMO CIR parameters, including its order. The proposed technique is based on the per survivor processing (PSP) methodology, it admits both blind and semiblind implementations, depending on the availability of pilot data, and is designed to work with time-selective channels. Besides the analytical derivation of the algorithm, we provide computer simulation results that illustrate the effectiveness of the resulting receiver}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } In the equalization of frequency-selective multiple-input multiple-output (MIMO) channels it is usually assumed that the length of the channel impulse response (CIR), also referred to as the channel order, is known. However, this is not true in most practical situations and, in order to avoid the serious performance degradation that occurs when the CIR length is underestimated, a channel with "more than enough" taps is usually considered. This possibly means overestimating the channel order, and is not desirable since the computational complexity of maximum likelihood sequence detection (MLSD) in frequency-selective channels grows exponentially with the channel order. In addition to that, the higher the channel order considered, the more the number of channel coefficients that need to be estimated from the same set of observations. In this paper, we introduce an algorithm for MLSD that incorporates the full estimation of the MIMO CIR parameters, including its order. The proposed technique is based on the per survivor processing (PSP) methodology, it admits both blind and semiblind implementations, depending on the availability of pilot data, and is designed to work with time-selective channels. Besides the analytical derivation of the algorithm, we provide computer simulation results that illustrate the effectiveness of the resulting receiver |

## 2007 |

Miguez, Joaquin; Artés-Rodríguez, Antonio Distributed Sequential Monte Carlo Algorithms for Node Localization and Target Tracking in Wireless Sensor Networks (Inproceeding) EURASIP Signal Processing Conference, EUSIPCO 2007, Poznan, 2007. @inproceedings{Miguez2007b, title = {Distributed Sequential Monte Carlo Algorithms for Node Localization and Target Tracking in Wireless Sensor Networks}, author = {Miguez, Joaquin and Artés-Rodríguez, Antonio}, url = {http://www.eurasip.org/Proceedings/Eusipco/Eusipco2007/Papers/b2l-b05.pdf}, year = {2007}, date = {2007-01-01}, booktitle = {EURASIP Signal Processing Conference, EUSIPCO 2007}, address = {Poznan}, abstract = {We address the problem of tracking a maneuvering target that moves along a region monitored by a wireless sensor network (WSN) whose nodes, including sensors and data fusion centers (DFCs), are located at unknown positions. Therefore, the target trajectory, its velocity and all node locations must be estimated jointly, without assuming the availability of any “beacons” with known location that can be used as a reference. We introduce a new method that comprises: (i) a combination of Monte Carlo optimization and iterated importance sampling to yield and initial population of node locations with high posterior probability (given data collected at the network startup) and (ii) a sequential Monte Carlo (SMC) algorithm for recursively tracking the target position and velocity and sequentially re-generating new populations of node positions as new observations become available. The resulting algorithm is implemented in a distributed fashion. Assuming that the communication capabilities of the DFCs enable them to share some data, each DFC can run an independent SMC algorithm and produce local estimates of the magnitudes of interest. Optimal data fusion is achieved by a linear combination of the local estimates with adequate weights. We illustrate the application of the algorithm in a network of power-aware sensors.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } We address the problem of tracking a maneuvering target that moves along a region monitored by a wireless sensor network (WSN) whose nodes, including sensors and data fusion centers (DFCs), are located at unknown positions. Therefore, the target trajectory, its velocity and all node locations must be estimated jointly, without assuming the availability of any “beacons” with known location that can be used as a reference. We introduce a new method that comprises: (i) a combination of Monte Carlo optimization and iterated importance sampling to yield and initial population of node locations with high posterior probability (given data collected at the network startup) and (ii) a sequential Monte Carlo (SMC) algorithm for recursively tracking the target position and velocity and sequentially re-generating new populations of node positions as new observations become available. The resulting algorithm is implemented in a distributed fashion. Assuming that the communication capabilities of the DFCs enable them to share some data, each DFC can run an independent SMC algorithm and produce local estimates of the magnitudes of interest. Optimal data fusion is achieved by a linear combination of the local estimates with adequate weights. We illustrate the application of the algorithm in a network of power-aware sensors. |

Vazquez, Manuel; Miguez, Joaquin Sequential MAP Equalization of MIMO Channels with Unknown Order (Inproceeding) IEEE/ITG Workshop on Smart Antennas (WSA 2007), Vienna, 2007. @inproceedings{Vazquez2007, title = {Sequential MAP Equalization of MIMO Channels with Unknown Order}, author = {Vazquez, Manuel A. and Miguez, Joaquin}, url = {http://www.eurasip.org/Proceedings/Ext/WSA07/papers/1569015393.pdf}, year = {2007}, date = {2007-01-01}, booktitle = {IEEE/ITG Workshop on Smart Antennas (WSA 2007)}, address = {Vienna}, abstract = {Practical equalization of multiple input multiple output (MIMO) channels poses several difficulties. Namely, it is well known that the complexity of maximum a posteriori (MAP) data detection grows exponentially with the number of inputs and the channel order, i.e., the length of the channel impulse response (CIR). Moreover, knowledge of the latter parameter is needed for reliable data detection, but its estimation is often a hard task and very few papers have tackled the problem. In this article, we propose the use of the sequential Monte Carlo (SMC) methodology to build quasi-MAP MIMO equalizers with polynomial complexity, that admit a parallel implementation and can handle the uncertainty in the channel order. In particular, we derive both optimal and complexity-constrained SMC algorithms for joint data detection, channel order and CIR estimation in frequency and time-selective MIMO channels. Computer simulation results are presented to illustrate the performance of the proposed techniques}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Practical equalization of multiple input multiple output (MIMO) channels poses several difficulties. Namely, it is well known that the complexity of maximum a posteriori (MAP) data detection grows exponentially with the number of inputs and the channel order, i.e., the length of the channel impulse response (CIR). Moreover, knowledge of the latter parameter is needed for reliable data detection, but its estimation is often a hard task and very few papers have tackled the problem. In this article, we propose the use of the sequential Monte Carlo (SMC) methodology to build quasi-MAP MIMO equalizers with polynomial complexity, that admit a parallel implementation and can handle the uncertainty in the channel order. In particular, we derive both optimal and complexity-constrained SMC algorithms for joint data detection, channel order and CIR estimation in frequency and time-selective MIMO channels. Computer simulation results are presented to illustrate the performance of the proposed techniques |

Vemula, Mahesh; Miguez, Joaquin; Artés-Rodríguez, Antonio A Sequential Monte Carlo Method for Target Tracking in an Asynchronous Wireless Sensor Network (Inproceeding) 2007 4th Workshop on Positioning, Navigation and Communication, pp. 49–54, IEEE, Hannover, 2007, ISBN: 1-4244-0870-9. @inproceedings{Vemula2007, title = {A Sequential Monte Carlo Method for Target Tracking in an Asynchronous Wireless Sensor Network}, author = {Vemula, Mahesh and Miguez, Joaquin and Artés-Rodríguez, Antonio}, url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=4167818 http://promultidis.tsc.uc3m.es/files/promultidisfiles/papers/WPNC07.pdf}, isbn = {1-4244-0870-9}, year = {2007}, date = {2007-01-01}, booktitle = {2007 4th Workshop on Positioning, Navigation and Communication}, pages = {49--54}, publisher = {IEEE}, address = {Hannover}, abstract = {Target tracking in a wireless sensor network (WSN) has become a relatively standard problem. The WSN typically consists of a collection of sensor nodes, which acquire physical data related to the target dynamics, and a fusion center (FC) where the available data are processed together to sequentially estimate the target state (its instantaneous location and velocity). Very often, tracking algorithms are designed under the assumption that the network is synchronous, i.e., that the local clocks of the sensor nodes and the FC are perfectly aligned or, at least, that their offsets are known. In this paper, we consider an asynchronous WSN, in which the local clocks of the sensors are misaligned and the corresponding offsets are unknown, and aim at designing recursive algorithms for optimal (Bayesian) tracking. In particular, we propose sequential Monte Carlo (SMC) techniques that enable the approximation of the joint posterior probability distribution of the target state and the set of local clock offsets by means of a discrete probability measure with a random support. From this approximation, estimates of the target position and velocity, as well as of the clock offsets, can be readily derived. We illustrate the validity of the proposed approach and assess the performance of the resulting algorithms by means of computer simulations}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Target tracking in a wireless sensor network (WSN) has become a relatively standard problem. The WSN typically consists of a collection of sensor nodes, which acquire physical data related to the target dynamics, and a fusion center (FC) where the available data are processed together to sequentially estimate the target state (its instantaneous location and velocity). Very often, tracking algorithms are designed under the assumption that the network is synchronous, i.e., that the local clocks of the sensor nodes and the FC are perfectly aligned or, at least, that their offsets are known. In this paper, we consider an asynchronous WSN, in which the local clocks of the sensors are misaligned and the corresponding offsets are unknown, and aim at designing recursive algorithms for optimal (Bayesian) tracking. In particular, we propose sequential Monte Carlo (SMC) techniques that enable the approximation of the joint posterior probability distribution of the target state and the set of local clock offsets by means of a discrete probability measure with a random support. From this approximation, estimates of the target position and velocity, as well as of the clock offsets, can be readily derived. We illustrate the validity of the proposed approach and assess the performance of the resulting algorithms by means of computer simulations |

## 2006 |

Miguez, Joaquin; Artés-Rodríguez, Antonio A Particle Filter for Beacon-Free Node Location and Target Tracking in Sensor Networks (Inproceeding) EURASIP Signal Processing Conference, EUSIPCO 2006, Florencia, 2006. @inproceedings{Miguez2006a, title = {A Particle Filter for Beacon-Free Node Location and Target Tracking in Sensor Networks}, author = {Miguez, Joaquin and Artés-Rodríguez, Antonio}, url = {http://www.eurasip.org/Proceedings/Eusipco/Eusipco2006/papers/1568982380.pdf}, year = {2006}, date = {2006-01-01}, booktitle = {EURASIP Signal Processing Conference, EUSIPCO 2006}, address = {Florencia}, abstract = {We address the problem of tracking a maneuvering target that moves along a region monitored by a sensor network, whose nodes, including both the sensors and the data fusion center (DFC), are located at unknown positions. Therefore, the node locations and the target track must be estimated jointly without the aid of beacons. We assume that, when the network is started, each sensor is able to detect the presence of other nodes within its range and transmit the resulting binary data to the DFC. After this startup phase, the sensor nodes just measure some physical magnitude related to the target position and/or velocity and transmit it to the DFC. At the DFC, a particle filtering (PF) algorithm is used to integrate all the collected data and produce on-line estimates of both the (static) sensor locations and the (dynamic) target trajectory. The validity of the method is illustrated by computer simulations of a network of power-aware sensors.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } We address the problem of tracking a maneuvering target that moves along a region monitored by a sensor network, whose nodes, including both the sensors and the data fusion center (DFC), are located at unknown positions. Therefore, the node locations and the target track must be estimated jointly without the aid of beacons. We assume that, when the network is started, each sensor is able to detect the presence of other nodes within its range and transmit the resulting binary data to the DFC. After this startup phase, the sensor nodes just measure some physical magnitude related to the target position and/or velocity and transmit it to the DFC. At the DFC, a particle filtering (PF) algorithm is used to integrate all the collected data and produce on-line estimates of both the (static) sensor locations and the (dynamic) target trajectory. The validity of the method is illustrated by computer simulations of a network of power-aware sensors. |

Miguez, Joaquin; Artés-Rodríguez, Antonio A Monte Carlo Method for Joint Node Location and Maneuvering Target Tracking in a Sensor Network (Inproceeding) 2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings, pp. IV–989–IV–992, IEEE, Toulouse, 2006, ISSN: 1520-6149. @inproceedings{Miguez2006b, title = {A Monte Carlo Method for Joint Node Location and Maneuvering Target Tracking in a Sensor Network}, author = {Miguez, Joaquin and Artés-Rodríguez, Antonio}, url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=1661137}, issn = {1520-6149}, year = {2006}, date = {2006-01-01}, booktitle = {2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings}, volume = {4}, pages = {IV--989--IV--992}, publisher = {IEEE}, address = {Toulouse}, abstract = {We address the problem of tracking a maneuvering target that moves along a region monitored by a sensor network, whose nodes (including both the sensors and any data fusion centers, DFCs) are located at unknown positions. Thus, the node locations and the target track must be estimated jointly without the aid of beacons. We assume that the network consists of a collection of sensors and at least four DFCs. Each DFC collects and integrates the sensor measurements and can exchange data with the other DFCs. Within this setup, we propose a three-stage Monte Carlo method to (i) acquire rough initial estimates of the network node locations, (ii) track the target and refine the node position estimates individually at each DFC and (iii) fuse the results obtained by all the DFCs. The validity of the method is illustrated by computer simulations of a network of power-aware sensors and exactly four DFCs.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } We address the problem of tracking a maneuvering target that moves along a region monitored by a sensor network, whose nodes (including both the sensors and any data fusion centers, DFCs) are located at unknown positions. Thus, the node locations and the target track must be estimated jointly without the aid of beacons. We assume that the network consists of a collection of sensors and at least four DFCs. Each DFC collects and integrates the sensor measurements and can exchange data with the other DFCs. Within this setup, we propose a three-stage Monte Carlo method to (i) acquire rough initial estimates of the network node locations, (ii) track the target and refine the node position estimates individually at each DFC and (iii) fuse the results obtained by all the DFCs. The validity of the method is illustrated by computer simulations of a network of power-aware sensors and exactly four DFCs. |

Vazquez, Manuel; Miguez, Joaquin Sequential MAP Equalization of MIMO Channels and Its Application to UWB Communications (Inproceeding) IEEE/ITG Int. Workshop on Smart Antennas (WSA'2006), Reisensburg, 2006. @inproceedings{Vazquez2006, title = {Sequential MAP Equalization of MIMO Channels and Its Application to UWB Communications}, author = {Vazquez, Manuel A. and Miguez, Joaquin}, url = {http://promultidis.tsc.uc3m.es/files/promultidisfiles/papers/Vazquez06.pdf http://www.researchgate.net/publication/228804790_Sequential_MAP_Equalization_of_MIMO_Channels_and_Its_Application_to_UWB_Communications}, year = {2006}, date = {2006-01-01}, booktitle = {IEEE/ITG Int. Workshop on Smart Antennas (WSA'2006)}, address = {Reisensburg}, abstract = {We introduce new sequential Monte Carlo (SMC) techniques for the maximum a posteriori (MAP) equalization of multiple input multiple output (MIMO) wireless channels. SMC methods have been recently proposed to tackle the MIMO equalization problem because of their potential to provide asymptotically optimal performance in terms of bit error rate and their suitability for implementation using parallel hardware. However, the existing algorithms are limited by their high computational complexity relative to the dimensions of the MIMO channel. The SMC equalizers in this paper overcome this drawback by means of a new sampling scheme that constrains the growth of the computational load to be of quadratic order with respect to the channel dimensions. We apply the new algorithms to the equalization of multi-antenna and multiuser (MU) ultra-wide band (UWB) communication systems and provide computer simulation results to illustrate their performance in both scenarios}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } We introduce new sequential Monte Carlo (SMC) techniques for the maximum a posteriori (MAP) equalization of multiple input multiple output (MIMO) wireless channels. SMC methods have been recently proposed to tackle the MIMO equalization problem because of their potential to provide asymptotically optimal performance in terms of bit error rate and their suitability for implementation using parallel hardware. However, the existing algorithms are limited by their high computational complexity relative to the dimensions of the MIMO channel. The SMC equalizers in this paper overcome this drawback by means of a new sampling scheme that constrains the growth of the computational load to be of quadratic order with respect to the channel dimensions. We apply the new algorithms to the equalization of multi-antenna and multiuser (MU) ultra-wide band (UWB) communication systems and provide computer simulation results to illustrate their performance in both scenarios |

Vazquez, Manuel; Miguez, Joaquin SMC Algorithms for Approximate MAP Equalization of MIMO Channels with Polynomial Complexity (Inproceeding) XIV European Signal Processing Conf. (EUSIPCO'2006), Florence, 2006. @inproceedings{Vazquez2006a, title = {SMC Algorithms for Approximate MAP Equalization of MIMO Channels with Polynomial Complexity}, author = {Vazquez, Manuel A. and Miguez, Joaquin}, url = {http://www.eurasip.org/Proceedings/Eusipco/Eusipco2006/papers/1568982361.pdf}, year = {2006}, date = {2006-01-01}, booktitle = {XIV European Signal Processing Conf. (EUSIPCO'2006)}, address = {Florence}, abstract = {Sequential Monte Carlo (SMC) schemes have been recently proposed in order to perform optimal equalization of mul- tiple input multiple output (MIMO) wireless channels. The main feaures of SMC techniques that make them appealing for the equalization problem are (a) their potential to pro- vide asymptotically optimal performance in terms of bit error rate and (b) their suitability for implementation using par- allel hardware. Nevertheless, existing SMC equalizers still exhibit a very high computational complexity, relative to the dimensions of the MIMO channel, which makes them use- less in practical situations. In this paper we introduce two new SMC equalizers whose computational load is only of polynomial order with respect to the channel dimensions, and avoid computationally heavy tasks such as matrix inversions. The performance of the proposed techniques is numerically illustrated by means of computer simulation}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Sequential Monte Carlo (SMC) schemes have been recently proposed in order to perform optimal equalization of mul- tiple input multiple output (MIMO) wireless channels. The main feaures of SMC techniques that make them appealing for the equalization problem are (a) their potential to pro- vide asymptotically optimal performance in terms of bit error rate and (b) their suitability for implementation using par- allel hardware. Nevertheless, existing SMC equalizers still exhibit a very high computational complexity, relative to the dimensions of the MIMO channel, which makes them use- less in practical situations. In this paper we introduce two new SMC equalizers whose computational load is only of polynomial order with respect to the channel dimensions, and avoid computationally heavy tasks such as matrix inversions. The performance of the proposed techniques is numerically illustrated by means of computer simulation |

## 2005 |

Bugallo, Monica; Miguez, Joaquin A Novel Adaptive Algorithm for Generalized Synchronization (Inproceeding) 2005 IEEE 61st Vehicular Technology Conference, pp. 1158–1161, IEEE, Stockholm, 2005, ISSN: 1550-2252. @inproceedings{Bugallo2005, title = {A Novel Adaptive Algorithm for Generalized Synchronization}, author = {Bugallo, Monica F. and Miguez, Joaquin}, url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=1543489}, issn = {1550-2252}, year = {2005}, date = {2005-01-01}, booktitle = {2005 IEEE 61st Vehicular Technology Conference}, volume = {2}, pages = {1158--1161}, publisher = {IEEE}, address = {Stockholm}, abstract = {We investigate a novel criterion for generalized synchronization that relies on the ability to characterize the synchronized signal in terms of a target probability density function (pdf). This pdf can be written in terms of the synchronization parameters so as to yield a target likelihood that can be maximized, hence the name maximum target likelihood (MTL) estimation for the proposed technique. After a brief description of the basics of the MTL principle, the criterion is applied to the joint adaptive recovery of the timing, phase offset, and received signal amplitude in a transmission system with QPSK modulation and raised-cosine pulse waveforms. We present computer simulations that show a performance improvement with respect to classical techniques based on maximum likelihood (ML) estimation for medium signal-to-noise ratio (SNR) values and impulsive background noise.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } We investigate a novel criterion for generalized synchronization that relies on the ability to characterize the synchronized signal in terms of a target probability density function (pdf). This pdf can be written in terms of the synchronization parameters so as to yield a target likelihood that can be maximized, hence the name maximum target likelihood (MTL) estimation for the proposed technique. After a brief description of the basics of the MTL principle, the criterion is applied to the joint adaptive recovery of the timing, phase offset, and received signal amplitude in a transmission system with QPSK modulation and raised-cosine pulse waveforms. We present computer simulations that show a performance improvement with respect to classical techniques based on maximum likelihood (ML) estimation for medium signal-to-noise ratio (SNR) values and impulsive background noise. |

Farina,; Miguez, Joaquin; Bugallo, Monica Novel Decision-Fusion Algorithms for Target Tracking Using Ad Hoc Networks (Inproceeding) 2005 IEEE 61st Vehicular Technology Conference, pp. 2556–2559, IEEE, Stockholm, 2005, ISSN: 1550-2252. @inproceedings{Farina2005, title = {Novel Decision-Fusion Algorithms for Target Tracking Using Ad Hoc Networks}, author = {Farina, N. and Miguez, Joaquin and Bugallo, Monica F.}, url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=1543796}, issn = {1550-2252}, year = {2005}, date = {2005-01-01}, booktitle = {2005 IEEE 61st Vehicular Technology Conference}, volume = {4}, pages = {2556--2559}, publisher = {IEEE}, address = {Stockholm}, abstract = {In this paper we address the problem of tracking an object which moves along a certain area monitored by an ad hoc network of wireless sensors. We investigate two approaches based on the sequential Monte Carlo (SMC) methodology: an approximate version of the classical sequential importance sampling (SIS) algorithm and a recently proposed SMC methodology called cost-reference particle filtering (CRPF), which provides more flexibility in the algorithm design. Computer simulations show the feasibility and performance of the proposed methods}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } In this paper we address the problem of tracking an object which moves along a certain area monitored by an ad hoc network of wireless sensors. We investigate two approaches based on the sequential Monte Carlo (SMC) methodology: an approximate version of the classical sequential importance sampling (SIS) algorithm and a recently proposed SMC methodology called cost-reference particle filtering (CRPF), which provides more flexibility in the algorithm design. Computer simulations show the feasibility and performance of the proposed methods |

Mariño, Inés; Miguez, Joaquin Gradient-Descent Methods for Parameter Estimation in Chaotic Systems (Inproceeding) ISPA 2005. Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis, 2005., pp. 440–445, IEEE, Zagreb, 2005, ISSN: 1845-5921. @inproceedings{Marino2005a, title = {Gradient-Descent Methods for Parameter Estimation in Chaotic Systems}, author = {Mariño, Inés and Miguez, Joaquin}, url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=1521331}, issn = {1845-5921}, year = {2005}, date = {2005-01-01}, booktitle = {ISPA 2005. Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis, 2005.}, pages = {440--445}, publisher = {IEEE}, address = {Zagreb}, abstract = {The rich nonlinear dynamics of chaos allows to model a broad variety of systems, including complex biological ones. The system of interest is usually observed through some time series and the modelization problem consists of adjusting the parameters of a model chaotic system until its dynamics is matched to the reference time series. In this paper, we describe a general methodology to adaptively select the values of the model parameters. Specifically, we assume that the observed time series are originated by a primary chaotic system with unknown parameters and we use it to drive a secondary chaotic system, so that both systems be coupled. The parameters of the secondary system are adaptively optimized (by a gradient-descent optimization of a suitable cost function) to make it follow the dynamics of the primary system. In this way, the secondary parameters are interpreted as estimates of the primary ones. We illustrate the application of the method by jointly estimating the complete parameter vector of a Lorenz system.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } The rich nonlinear dynamics of chaos allows to model a broad variety of systems, including complex biological ones. The system of interest is usually observed through some time series and the modelization problem consists of adjusting the parameters of a model chaotic system until its dynamics is matched to the reference time series. In this paper, we describe a general methodology to adaptively select the values of the model parameters. Specifically, we assume that the observed time series are originated by a primary chaotic system with unknown parameters and we use it to drive a secondary chaotic system, so that both systems be coupled. The parameters of the secondary system are adaptively optimized (by a gradient-descent optimization of a suitable cost function) to make it follow the dynamics of the primary system. In this way, the secondary parameters are interpreted as estimates of the primary ones. We illustrate the application of the method by jointly estimating the complete parameter vector of a Lorenz system. |

Miguez, Joaquin; Artés-Rodríguez, Antonio Monte Carlo Algorithms for Tracking a Maneuvering Target using a Network of Mobile Sensors (Inproceeding) 1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005., pp. 89–92, IEEE, Puerto Vallarta, 2005, ISBN: 0-7803-9322-8. @inproceedings{Miguez2005a, title = {Monte Carlo Algorithms for Tracking a Maneuvering Target using a Network of Mobile Sensors}, author = {Miguez, Joaquin and Artés-Rodríguez, Antonio}, url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=1574191}, isbn = {0-7803-9322-8}, year = {2005}, date = {2005-01-01}, booktitle = {1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005.}, pages = {89--92}, publisher = {IEEE}, address = {Puerto Vallarta}, abstract = {We address the problem of tracking a maneuvering target that moves over a two-dimensional region using a network of mobile binary sensors. The transmission of binary decisions (presence or absence of the target within the sensor range) is advantageous because it reduces energy consumption considerably. Also, the use of mobile sensors allows tracking the target over a large area with only a limited number of devices. We introduce two algorithms, based on the sequential Monte Carlo methodology, that track the target and the sensors (whose position is also unknown) jointly. The performance of the trackers is illustrated by means of computer simulations}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } We address the problem of tracking a maneuvering target that moves over a two-dimensional region using a network of mobile binary sensors. The transmission of binary decisions (presence or absence of the target within the sensor range) is advantageous because it reduces energy consumption considerably. Also, the use of mobile sensors allows tracking the target over a large area with only a limited number of devices. We introduce two algorithms, based on the sequential Monte Carlo methodology, that track the target and the sensors (whose position is also unknown) jointly. The performance of the trackers is illustrated by means of computer simulations |

Miguez, Joaquin; Bugallo, Monica; Djuric, Petar Decision Fusion for Distributed Target Tracking using Cost Reference Particle Filtering (Inproceeding) XIII European Signal Processing Conf. (EUSIPCO 2005), Antalya, 2005. @inproceedings{Miguez2005b, title = {Decision Fusion for Distributed Target Tracking using Cost Reference Particle Filtering}, author = {Miguez, Joaquin and Bugallo, Monica F. and Djuric, Petar M.}, url = {http://signal.ee.bilkent.edu.tr/defevent/papers/cr1543.pdf}, year = {2005}, date = {2005-01-01}, booktitle = {XIII European Signal Processing Conf. (EUSIPCO 2005)}, address = {Antalya}, abstract = {In this paper, we consider the problem of tracking an object which moves along a certain 2-dimensional area monitored by a network of wireless sensors. We propose a novel decision fusion algorithm for target tracking based on a recently proposed sequential Monte Carlo (SMC) methodology called cost-reference particle filtering (CRPF). Computer simulations reveal the feasibility of the proposed method.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } In this paper, we consider the problem of tracking an object which moves along a certain 2-dimensional area monitored by a network of wireless sensors. We propose a novel decision fusion algorithm for target tracking based on a recently proposed sequential Monte Carlo (SMC) methodology called cost-reference particle filtering (CRPF). Computer simulations reveal the feasibility of the proposed method. |

Miguez, Joaquin; Bugallo, Monica; Djuric, Petar Novel Particle Filtering Algorithms for Fixed Parameter Estimation in Dynamic Systems (Inproceeding) ISPA 2005. Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis, 2005., pp. 46–51, IEEE, Zagreb, 2005, ISSN: 1845-5921. @inproceedings{Miguez2005c, title = {Novel Particle Filtering Algorithms for Fixed Parameter Estimation in Dynamic Systems}, author = {Miguez, Joaquin and Bugallo, Monica F. and Djuric, Petar M.}, url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=1521261}, issn = {1845-5921}, year = {2005}, date = {2005-01-01}, booktitle = {ISPA 2005. Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis, 2005.}, pages = {46--51}, publisher = {IEEE}, address = {Zagreb}, abstract = {Standard particle filters cannot handle dynamic systems with unknown fixed parameters. In this paper, we extend the recently proposed cost-reference particle filtering methodology (CRPF) to jointly estimate the time-varying state and the static parameters of a dynamic system. In particular, we introduce three strategies that allow assigning costs to the random samples in the state-space independently of the fixed parameters. Asymptotic results that illuminate the relationships among the methods are derived, and computer simulation results are presented to illustrate their practical implementation in a vehicle navigation problem}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Standard particle filters cannot handle dynamic systems with unknown fixed parameters. In this paper, we extend the recently proposed cost-reference particle filtering methodology (CRPF) to jointly estimate the time-varying state and the static parameters of a dynamic system. In particular, we introduce three strategies that allow assigning costs to the random samples in the state-space independently of the fixed parameters. Asymptotic results that illuminate the relationships among the methods are derived, and computer simulation results are presented to illustrate their practical implementation in a vehicle navigation problem |

Miguez, Joaquin; Xu, Shanshan; Bugallo, Monica; Djuric, Petar Joint Estimation of States and Transition Functions of Dynamic Systems Using Cost-Reference Particle Filtering (Inproceeding) Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005., pp. 361–364, IEEE, Philadelphia, 2005, ISSN: 1520-6149. @inproceedings{Miguez2005d, title = {Joint Estimation of States and Transition Functions of Dynamic Systems Using Cost-Reference Particle Filtering}, author = {Miguez, Joaquin and Xu, Shanshan and Bugallo, Monica F. and Djuric, Petar M.}, url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=1416020}, issn = {1520-6149}, year = {2005}, date = {2005-01-01}, booktitle = {Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005.}, volume = {4}, pages = {361--364}, publisher = {IEEE}, address = {Philadelphia}, abstract = {The recently introduced cost-reference particle filter (CRPF) methodology allows for recursive estimation of unobserved states of dynamic systems without a priori knowledge of probability distributions of the noise in the system. We use CRPFs in problems where we eliminate one more strong assumption about the state space model, the one of knowing the function governing the state evolution. We replace this function by a linearly combined set of basis functions where the linear combination coefficients are unknown. We show how CRPFs can be modified to cope with this scenario and demonstrate their performance for positioning a moving vehicle in a two-dimensional space}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } The recently introduced cost-reference particle filter (CRPF) methodology allows for recursive estimation of unobserved states of dynamic systems without a priori knowledge of probability distributions of the noise in the system. We use CRPFs in problems where we eliminate one more strong assumption about the state space model, the one of knowing the function governing the state evolution. We replace this function by a linearly combined set of basis functions where the linear combination coefficients are unknown. We show how CRPFs can be modified to cope with this scenario and demonstrate their performance for positioning a moving vehicle in a two-dimensional space |

Vazquez, Manuel; Miguez, Joaquin A Complexity-Constrained Particle Filtering Algorithm for MAP Equalization of Frequency-Selective MIMO Channels (Inproceeding) Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005., pp. 477–480, IEEE, 2005, ISSN: 1520-6149. @inproceedings{Vazquez2005, title = {A Complexity-Constrained Particle Filtering Algorithm for MAP Equalization of Frequency-Selective MIMO Channels}, author = {Vazquez, Manuel A. and Miguez, Joaquin}, url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=1415750}, issn = {1520-6149}, year = {2005}, date = {2005-01-01}, booktitle = {Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005.}, volume = {3}, pages = {477--480}, publisher = {IEEE}, abstract = {Sequential Monte Carlo (SMC) schemes have recently been proposed in order to perform optimal equalization of multiple input multiple output (MIMO) wireless channels. Unfortunately, for each simulated data sample, the complexity of existing algorithms grows exponentially with the number of input data streams. We propose a novel SMC MIMO channel equalizer that avoids this limitation. An adequate design of the data sampling scheme leads to a reduction of the computational load per sample, which becomes linear in the number of channel inputs. Computer simulations that illustrate the nearly optimal bit error rate of the proposed SMC equalizer are presented}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Sequential Monte Carlo (SMC) schemes have recently been proposed in order to perform optimal equalization of multiple input multiple output (MIMO) wireless channels. Unfortunately, for each simulated data sample, the complexity of existing algorithms grows exponentially with the number of input data streams. We propose a novel SMC MIMO channel equalizer that avoids this limitation. An adequate design of the data sampling scheme leads to a reduction of the computational load per sample, which becomes linear in the number of channel inputs. Computer simulations that illustrate the nearly optimal bit error rate of the proposed SMC equalizer are presented |

Vazquez, Manuel; Miguez, Joaquin; Bugallo, Monica Novel SMC Techniques for Blind Equalization of Flat-Fading MIMO Channels (Inproceeding) 2005 IEEE 61st Vehicular Technology Conference, pp. 567–571, IEEE, Stockholm, 2005, ISSN: 1550-2252. @inproceedings{Vazquez2005a, title = {Novel SMC Techniques for Blind Equalization of Flat-Fading MIMO Channels}, author = {Vazquez, Manuel A. and Miguez, Joaquin and Bugallo, Monica F.}, url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=1543355}, issn = {1550-2252}, year = {2005}, date = {2005-01-01}, booktitle = {2005 IEEE 61st Vehicular Technology Conference}, volume = {1}, pages = {567--571}, publisher = {IEEE}, address = {Stockholm}, abstract = {We investigate two novel blind receivers for joint channel estimation and data detection in flat-fading multiple-input multiple-output (MIMO) communication systems. Both schemes are based on the sequential Monte Carlo (SMC) methodology, also known as particle filtering, and attain asymptotically optimal performance, both in terms of channel estimation and data detection. The most salient feature of the new SMC receivers is that their computational complexity growth is polynomial in the number of input data streams, in contrast to the complexity of most previously proposed SMC algorithms for blind MIMO equalization, which increases exponentially with the number of input streams. We present simulation results to show that the reduction in complexity is attained with a negligible impact on performance}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } We investigate two novel blind receivers for joint channel estimation and data detection in flat-fading multiple-input multiple-output (MIMO) communication systems. Both schemes are based on the sequential Monte Carlo (SMC) methodology, also known as particle filtering, and attain asymptotically optimal performance, both in terms of channel estimation and data detection. The most salient feature of the new SMC receivers is that their computational complexity growth is polynomial in the number of input data streams, in contrast to the complexity of most previously proposed SMC algorithms for blind MIMO equalization, which increases exponentially with the number of input streams. We present simulation results to show that the reduction in complexity is attained with a negligible impact on performance |

# Conference Papers (2016-2004)

## 2016 |

A Novel Algorithm for Adapting the Number of Particles in Particle Filtering (Inproceeding) 2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM), pp. 1–5, IEEE, Rio de Janeiro, 2016, ISBN: 978-1-5090-2103-1. |

Online Adaptation of the Number of Particles of Sequential Monte Carlo Methods (Inproceeding) IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2016), Shanghai, 2016. |

## 2015 |

A Nonlinear Population Monte Carlo Scheme for Bayesian Parameter Estimation in a Stochastic Intercellular Network Model (Inproceeding) 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), pp. 497–500, IEEE, Cancun, 2015, ISBN: 978-1-4799-1963-5. |

Particle filtering for Bayesian parameter estimation in a high dimensional state space model (Inproceeding) 2015 23rd European Signal Processing Conference (EUSIPCO), pp. 1241–1245, IEEE, Nice, 2015, ISBN: 978-0-9928-6263-3. |

## 2014 |

On the uniform asymptotic convergence of a distributed particle filter (Inproceeding) 2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM), pp. 241–244, IEEE, A Coruña, 2014, ISBN: 978-1-4799-1481-4. |

Nested Particle Filters for Sequential Parameter Estimation in Discrete-time State-space Models (Inproceeding) SIAM 2014 Conference on Uncertainty Quantification, Savannah, 2014. |

## 2013 |

A Population Monte Carlo Scheme for Computational Inference in High Dimensional Spaces (Inproceeding) 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6318–6322, IEEE, Vancouver, 2013, ISSN: 1520-6149. |

Robust Mixture Population Monte Carlo Scheme with Adaptation of the Number of Components (Inproceeding) European Signal Processing Conference (EUSIPCO) 2013, Marrakech, 2013. |

Particle Filtering with Transformed Weights (Inproceeding) 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), pp. 364–367, IEEE, St. Martin, 2013, ISBN: 978-1-4673-3146-3. |

## 2012 |

Importance Sampling with Transformed Weights (Inproceeding) Data Assimilation Workshop, Oxford–Man Institute, Oxford, 2012. |

## 2011 |

A Parallel Resampling Scheme and its Application to Distributed Particle Filtering in Wireless Networks (Inproceeding) 2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), pp. 81–84, IEEE, San Juan, 2011, ISBN: 978-1-4577-2105-2. |

Efficient Distributed Resampling for Particle Filters (Inproceeding) 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3772–3775, IEEE, Prague, 2011, ISSN: 1520-6149. |

A Population Monte Carlo Method for Bayesian Inference and its Application to Stochastic Kinetic Models (Inproceeding) EUSIPCO 2011, Barcelona, 2011. |

On the Optimization of Transportation Routes with Multiple Destinations in Random Networks (Inproceeding) 2011 IEEE Statistical Signal Processing Workshop (SSP), pp. 349–352, IEEE, Nice, 2011, ISBN: 978-1-4577-0569-4. |

## 2010 |

A Model-Switching Sequential Monte Carlo Algorithm for Indoor Tracking with Experimental RSS Data (Inproceeding) 2010 International Conference on Indoor Positioning and Indoor Navigation, pp. 1–8, IEEE, Zurich, 2010, ISBN: 978-1-4244-5862-2. |

Evaluation of a Method's Robustness (Inproceeding) 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 3598–3601, IEEE, Dallas, 2010, ISSN: 1520-6149. |

Maximum a Posteriori Voice Conversion Using Sequential Monte Carlo Methods (Inproceeding) Eleventh Annual Conference of the International Speech Communication Association (INTERSPEECH), Makuhari, Chiba, Japan, 2010. |

A Rejection Sampling Scheme for Posterior Probability Distributions via the Ratio-of-Uniforms Method (Inproceeding) 18th European Signal Processing Conference (EUSIPCO-2010), Aalborg, 2010. |

Adaptive MLSD for MIMO Transmission Systems with Unknown Subchannel Orders (Inproceeding) 2010 7th International Symposium on Wireless Communication Systems, pp. 451–455, IEEE, York, 2010, ISSN: 2154-0217. |

## 2009 |

A Multi-Model Particle Filtering Algorithm for Indoor Tracking of Mobile Terminals Using RSS Data (Inproceeding) 2009 IEEE International Conference on Control Applications, pp. 1702–1707, IEEE, Saint Petersburg, 2009, ISBN: 978-1-4244-4601-8. |

Cost-Reference Particle Filters and Fusion of Information (Inproceeding) 2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, pp. 286–291, IEEE, Marco Island, FL, 2009. |

Measuring the Robustness of Sequential Methods (Inproceeding) 2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, pp. 29–32, IEEE, Aruba, Dutch Antilles, 2009, ISBN: 978-1-4244-5179-1. |

Model Assessment with Kolmogorov-Smirnov Statistics (Inproceeding) 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 2973–2976, IEEE, Taipei, 2009, ISSN: 1520-6149. |

Particle Filtering in the Presence of Outliers (Inproceeding) 2009 IEEE/SP 15th Workshop on Statistical Signal Processing, pp. 33–36, IEEE, Cardiff, 2009, ISBN: 978-1-4244-2709-3. |

An Adaptive Accept/Reject Sampling Algorithm for Posterior Probability Distributions (Inproceeding) 2009 IEEE/SP 15th Workshop on Statistical Signal Processing, pp. 45–48, IEEE, Cardiff, 2009, ISBN: 978-1-4244-2709-3. |

New Accept/Reject Methods for Independent Sampling from Posterior Probability Distributions (Inproceeding) 17th European Signal Processing Conference (EUSIPCO 2009), Glasgow, 2009. |

A Novel Rejection Sampling Scheme for Posterior Probability Distributions (Inproceeding) 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 2921–2924, IEEE, Taipei, 2009, ISSN: 1520-6149. |

Sequential Monte Carlo Optimization Using Artificial State-Space Models (Inproceeding) 2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, pp. 268–273, IEEE, Marco Island, FL, 2009. |

## 2008 |

Analysis of a Sequential Monte Carlo Optimization Methodology (Inproceeding) 16th European Signal Processing Conference (EUSIPCO 2008, Lausanne, 2008. |

A Per-Survivor Processing Algorithm for Maximum Likelihood Equalization of MIMO Channels with Unknown Order (Inproceeding) 2008 International ITG Workshop on Smart Antennas, pp. 387–391, IEEE, Vienna, 2008, ISBN: 978-1-4244-1756-8. |

## 2007 |

Distributed Sequential Monte Carlo Algorithms for Node Localization and Target Tracking in Wireless Sensor Networks (Inproceeding) EURASIP Signal Processing Conference, EUSIPCO 2007, Poznan, 2007. |

Sequential MAP Equalization of MIMO Channels with Unknown Order (Inproceeding) IEEE/ITG Workshop on Smart Antennas (WSA 2007), Vienna, 2007. |

A Sequential Monte Carlo Method for Target Tracking in an Asynchronous Wireless Sensor Network (Inproceeding) 2007 4th Workshop on Positioning, Navigation and Communication, pp. 49–54, IEEE, Hannover, 2007, ISBN: 1-4244-0870-9. |

## 2006 |

A Particle Filter for Beacon-Free Node Location and Target Tracking in Sensor Networks (Inproceeding) EURASIP Signal Processing Conference, EUSIPCO 2006, Florencia, 2006. |

A Monte Carlo Method for Joint Node Location and Maneuvering Target Tracking in a Sensor Network (Inproceeding) 2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings, pp. IV–989–IV–992, IEEE, Toulouse, 2006, ISSN: 1520-6149. |

Sequential MAP Equalization of MIMO Channels and Its Application to UWB Communications (Inproceeding) IEEE/ITG Int. Workshop on Smart Antennas (WSA'2006), Reisensburg, 2006. |

SMC Algorithms for Approximate MAP Equalization of MIMO Channels with Polynomial Complexity (Inproceeding) XIV European Signal Processing Conf. (EUSIPCO'2006), Florence, 2006. |

## 2005 |

A Novel Adaptive Algorithm for Generalized Synchronization (Inproceeding) 2005 IEEE 61st Vehicular Technology Conference, pp. 1158–1161, IEEE, Stockholm, 2005, ISSN: 1550-2252. |

Novel Decision-Fusion Algorithms for Target Tracking Using Ad Hoc Networks (Inproceeding) 2005 IEEE 61st Vehicular Technology Conference, pp. 2556–2559, IEEE, Stockholm, 2005, ISSN: 1550-2252. |

Gradient-Descent Methods for Parameter Estimation in Chaotic Systems (Inproceeding) ISPA 2005. Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis, 2005., pp. 440–445, IEEE, Zagreb, 2005, ISSN: 1845-5921. |

Monte Carlo Algorithms for Tracking a Maneuvering Target using a Network of Mobile Sensors (Inproceeding) 1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005., pp. 89–92, IEEE, Puerto Vallarta, 2005, ISBN: 0-7803-9322-8. |

Decision Fusion for Distributed Target Tracking using Cost Reference Particle Filtering (Inproceeding) XIII European Signal Processing Conf. (EUSIPCO 2005), Antalya, 2005. |

Novel Particle Filtering Algorithms for Fixed Parameter Estimation in Dynamic Systems (Inproceeding) ISPA 2005. Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis, 2005., pp. 46–51, IEEE, Zagreb, 2005, ISSN: 1845-5921. |

Joint Estimation of States and Transition Functions of Dynamic Systems Using Cost-Reference Particle Filtering (Inproceeding) Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005., pp. 361–364, IEEE, Philadelphia, 2005, ISSN: 1520-6149. |

A Complexity-Constrained Particle Filtering Algorithm for MAP Equalization of Frequency-Selective MIMO Channels (Inproceeding) Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005., pp. 477–480, IEEE, 2005, ISSN: 1520-6149. |

Novel SMC Techniques for Blind Equalization of Flat-Fading MIMO Channels (Inproceeding) 2005 IEEE 61st Vehicular Technology Conference, pp. 567–571, IEEE, Stockholm, 2005, ISSN: 1550-2252. |