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37
Marginalized particle filters for mixed linear/nonlinear statespace models
 IEEE Transactions on Signal Processing
, 2005
"... Abstract—The particle filter offers a general numerical tool to approximate the posterior density function for the state in nonlinear and nonGaussian filtering problems. While the particle filter is fairly easy to implement and tune, its main drawback is that it is quite computer intensive, with th ..."
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Cited by 112 (33 self)
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Abstract—The particle filter offers a general numerical tool to approximate the posterior density function for the state in nonlinear and nonGaussian filtering problems. While the particle filter is fairly easy to implement and tune, its main drawback is that it is quite computer intensive, with the computational complexity increasing quickly with the state dimension. One remedy to this problem is to marginalize out the states appearing linearly in the dynamics. The result is that one Kalman filter is associated with each particle. The main contribution in this paper is the derivation of the details for the marginalized particle filter for a general nonlinear statespace model. Several important special cases occurring in typical signal processing applications will also be discussed. The marginalized particle filter is applied to an integrated navigation system for aircraft. It is demonstrated that the complete highdimensional system can be based on a particle filter using marginalization for all but three states. Excellent performance on real flight data is reported. Index Terms—Kalman filter, marginalization, navigation systems, nonlinear systems, particle filter, state estimation. I.
Gaussian sum particle filtering
 Signal Processing 51
, 2003
"... Abstract—In this paper, we use the Gaussian particle filter introduced in a companion paper to build several types of Gaussian sum particle filters. These filters approximate the filtering and predictive distributions by weighted Gaussian mixtures and are basically banks of Gaussian particle filters ..."
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Cited by 70 (3 self)
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Abstract—In this paper, we use the Gaussian particle filter introduced in a companion paper to build several types of Gaussian sum particle filters. These filters approximate the filtering and predictive distributions by weighted Gaussian mixtures and are basically banks of Gaussian particle filters. Then, we extend the use of Gaussian particle filters and Gaussian sum particle filters to dynamic state space (DSS) models with nonGaussian noise. With nonGaussian noise approximated by Gaussian mixtures, the nonGaussian noise models are approximated by banks of Gaussian noise models, and Gaussian mixture filters are developed using algorithms developed for Gaussian noise DSS models. 1 As a result, problems involving heavytailed densities can be conveniently addressed. Simulations are presented to exhibit the application of the framework developed herein, and the performance of the algorithms is examined. Index Terms—Dynamic statespace models, extended Kalman
Particle Filtering for Multisensor Data Fusion with Switching Observation Models. Application to Land Vehicle
 Positioning, in &quot;IEEE transactions on Signal Processing
, 2006
"... This paper concerns the sequential estimation of a hidden state vector from noisy observations delivered by several sensors. Different from the standard framework, we assume here that the sensors may switch autonomously between different sensor states, that is, between different observation models. ..."
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Cited by 30 (4 self)
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This paper concerns the sequential estimation of a hidden state vector from noisy observations delivered by several sensors. Different from the standard framework, we assume here that the sensors may switch autonomously between different sensor states, that is, between different observation models. This includes sensor failure or sensor functioning conditions change. In our model, sensor states are represented by discrete latent variables, which prior probabilities are Markovian. We propose a family of efficient particle filters, for both synchronous and asynchronous sensor observations, as well as for important special cases. Moreover, we discuss connections with previous works. Finally, we study thoroughly a wheel land vehicle positioning problem where the GPS information may be unreliable because of multipath/masking effects. EDICS: SEN FUS
Architectures for Efficient Implementation of Particle Filters
, 2004
"... Particle filters are sequential Monte Carlo methods that are used in numerous problems where timevarying signals must be presented in real time and where the objective is to estimate various unknowns of the signal and/or detect events described by the signals. The standard solutions of such proble ..."
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Cited by 22 (0 self)
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Particle filters are sequential Monte Carlo methods that are used in numerous problems where timevarying signals must be presented in real time and where the objective is to estimate various unknowns of the signal and/or detect events described by the signals. The standard solutions of such problems in many applications are based on the Kalman filters or extended Kalman filters. In situations when the problems are nonlinear or the noise that distorts the signals is nonGaussian, the Kalman filters provide a solution that may be far from optimal. Particle filters are an intriguing alternative to the Kalman filters due to their excellent performance in very di#cult problems including communications, signal processing, navigation, and computer vision. Hence, particle filters have been the focus of wide research recently and immense literature can be found on their theory. Most of these works recognize the complexity and computational intensity of these filters, but there has been no e#ort directed toward the implementation of these filters in hardware. The objective of this dissertation is to develop, design, and build e#cient hardware for particle filters, and thereby bring them closer to practical applications. The fact that particle filters outperform most of the traditional filtering methods in many complex practical scenarios, coupled with the challenges related to decreasing their computational complexity and improving realtime performance, makes this work worthwhile. The main
A Bayesian approach to nonlinear probit gene selection and classification
, 2004
"... We considerth problem of gene selection and classification based on th expression data. Specifically, we propose a bootstrap Bayesian gene selectionmetht for nonlinear probit regression. A binomial probit regression modelwith data augmentation is used to transform th binomial problem into a sequence ..."
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Cited by 11 (2 self)
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We considerth problem of gene selection and classification based on th expression data. Specifically, we propose a bootstrap Bayesian gene selectionmetht for nonlinear probit regression. A binomial probit regression modelwith data augmentation is used to transform th binomial problem into a sequence of smoothc. problems.Th probit regressor is approximated as a nonlinear combination of th genes. A Gibbs sampler is employed to find th strongest genes. Some numericaltechcalcS to speed up th computation are discussed. WethM develop a nonlinear probit Bayesian classifier consisting of a linear term plus a nonlinear term,th parameters ofwhSz are estimated usingth sequential Monte Carlo techcGSqG Thch newmethGS are applied to analyze several data sets, includingth hludingc breast cancer data,th small round bluecell tumor data, and th acute leukemia tumor data.Th experimental resultsshu th proposedmethse can effectively find important genes whsc are consistentwith th existing biological belief, and th classification accuracies are very hryc Some robustness and sensitivity properties of th proposedmethse are also discussed to dealwith noisy microarray data.
A New Class of Soft MIMO Demodulation Algorithms
, 2003
"... We propose a new class of softinput softoutput demodulation schemes for multipleinput multipleoutput (MIMO) channels, based on the sequential Monte Carlo (SMC) framework under both stochastic and deterministic settings. The stochastic SMC sampler generates MIMO symbol samples based on importance ..."
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Cited by 11 (1 self)
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We propose a new class of softinput softoutput demodulation schemes for multipleinput multipleoutput (MIMO) channels, based on the sequential Monte Carlo (SMC) framework under both stochastic and deterministic settings. The stochastic SMC sampler generates MIMO symbol samples based on importance sampling and resampling techniques, whereas the deterministic SMC approach recursively performs exploration and selection steps in a greedy manner. By exploiting the artificial sequential structure of the existing simple BellLabs layered spacetime (BLAST) detection method based on nulling and cancellation, the proposed algorithms achieve an error probability performance that is orders of magnitude better than the traditional BLAST detection schemes while maintaining a low computational complexity. In fact, the new methods offer performance comparable with that of the sphere decoding algorithm without attendant increase in complexity. More importantly, being softinput softoutput in nature, both the stochastic and deterministic SMC detectors can be employed as the firststage demodulator in a turbo receiver in coded MIMO systems. Such a turbo receiver successively improves the receiver performance by iteratively exchanging the socalled extrinsic information between the soft outer channel decoder and the inner soft MIMO demodulator under both known channel state and unknown channel state scenarios. Computer simulation results are provided to demonstrate the performance of the proposed algorithms.
Extending expectation propagation for graphical models
, 2005
"... Graphical models have been widely used in many applications, ranging from human behavior recognition to wireless signal detection. However, efficient inference and learning techniques for graphical models are needed to handle complex models, such as hybrid Bayesian networks. This thesis proposes ext ..."
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Cited by 9 (5 self)
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Graphical models have been widely used in many applications, ranging from human behavior recognition to wireless signal detection. However, efficient inference and learning techniques for graphical models are needed to handle complex models, such as hybrid Bayesian networks. This thesis proposes extensions of expectation propagation, a powerful generalization of loopy belief propagation, to develop efficient inference and learning algorithms for graphical models. The first two chapters of the thesis present inference algorithms for generative graphical models, and the next two propose learning algorithms for conditional graphical models. First, the thesis proposes a windowbased EP smoothing algorithm, as an alternative to batch EP, for hybrid dynamic Bayesian networks. For an application to digital wireless communications, windowbased EP smoothing achieves estimation accuracy comparable to sequential Monte Carlo methods, but with more than 10 times less computational cost. Second, it combines treestructured EP approximation with the junction tree algorithm
WaveletBased Sequential Monte Carlo Blind Receivers In Fading Channels with Unknowns Channel Statistics
 IEEE Trans. Sig. Proc
, 2004
"... Recently, an adaptive Bayesian receiver for blind detection in flatfading channels was developed by the present authors, based on the sequential Monte Carlo methodology. That work is built on a parametric modeling of the fading process in the form of a statespace model and assumes the knowledge of ..."
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Cited by 8 (4 self)
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Recently, an adaptive Bayesian receiver for blind detection in flatfading channels was developed by the present authors, based on the sequential Monte Carlo methodology. That work is built on a parametric modeling of the fading process in the form of a statespace model and assumes the knowledge of the secondorder statistics of the fading channel. In this paper, we develop a nonparametric approach to the problem of blind detection in fading channels, without assuming any knowledge of the channel statistics. The basic idea is to decompose the fading process using a wavelet basis and to use the sequential Monte Carlo technique to track both the wavelet coefficients and the transmitted symbols. A novel resamplingbased wavelet shrinkage technique is proposed to dynamically choose the number of wavelet coefficients to best fit the fading process. Under such a framework, blind detectors for both flatfading channels and frequencyselective fading channels are developed. Simulation results are provided to demonstrate the excellent performance of the proposed blind adaptive receivers.
Blind Equalization of FrequencySelective Channels by Sequential Importance Sampling
 in IEEE Transactions on Signal Processing
, 2004
"... Abstract—This paper introduces a novel blind equalization algorithm for frequencyselective channels based on a Bayesian formulation of the problem and the sequential importance sampling (SIS) technique. SIS methods rely on building a Monte Carlo (MC) representation of the probability distributio ..."
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Cited by 7 (1 self)
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Abstract—This paper introduces a novel blind equalization algorithm for frequencyselective channels based on a Bayesian formulation of the problem and the sequential importance sampling (SIS) technique. SIS methods rely on building a Monte Carlo (MC) representation of the probability distribution of interest that consists of a set of samples (usually called particles) and associated weights computed recursively in time. We elaborate on this principle to derive blind sequential algorithms that perform maximum a posteriori (MAP) symbol detection without explicit estimation of the channel parameters. In particular, we start with a basic algorithm that only requires the a priori knowledge of the model order of the channel, but we subsequently relax this assumption and investigate novel procedures to handle model order uncertainty as well. The bit error rate (BER) performance of the proposed Bayesian equalizers is evaluated and compared with that of other equalizers through computer simulations. Index Terms—Bayesian estimation, blind equalization, Monte Carlo methods, SIS algorithm.
Expectation Propagation for Signal Detection in FlatFading Channels
 Proceedings of IEEE International Symposium on Information Theory
, 2003
"... In this paper, we propose a new Bayesian receiver for signal detection in flatfading channels. ..."
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Cited by 7 (4 self)
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In this paper, we propose a new Bayesian receiver for signal detection in flatfading channels.