Particle filtering with pairwise Markov processes

Desbouvries, François; Pieczynski, Wojciech

doi:10.1109/icassp.2003.1201779

Cited by 7 publications

(8 citation statements)

References 10 publications

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“…Clearly, for this case, the hidden states and the observations form jointly a Markov chain [14]. This joint Markov chain model was considered in [13] for the particle filter with correlated Gaussian noises.…”

Section: A Type I Dependencymentioning

confidence: 99%

Particle Filtering With Dependent Noise Processes

Saha

Gustafsson

2012

IEEE Trans. Signal Process.

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Abstract-Modeling physical systems often leads to discrete time state space models with dependent process and measurement noises. For linear Gaussian models, the Kalman filter handles this case, as is well described in literature. However, for nonlinear or non-Gaussian models, the particle filter as described in literature provides a general solution only for the case of independent noise. Here, we present an extended theory of the particle filter for dependent noises with the following key contributions: (i) The optimal proposal distribution is derived. (ii) The special case of Gaussian noise in nonlinear models is treated in detail, leading to a concrete algorithm that is as easy to implement as the corresponding Kalman filter. (iii) The marginalized (RaoBlackwellized) particle filter, handling linear Gaussian substructures in the model in an efficient way, is extended to dependent noise. Finally, (iv) the parameters of a joint Gaussian distribution of the noise processes are estimated jointly with the state in a recursive way.

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Section: A Type I Dependencymentioning

confidence: 99%

Particle Filtering With Dependent Noise Processes

Saha

Gustafsson

2012

IEEE Trans. Signal Process.

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“…In [22], the authors prove that TMMs are more general than PMMs and PMMs more general than HMMs. Furthermore, the authors show that classical particle filtering approaches can be extended to both PMMs and TMMs.…”

Section: Further Discussionmentioning

confidence: 97%

Evolutionary Computing and Particle Filtering: A Hardware-Based Motion Estimation System

Rodríguez

Moreno

2015

IEEE Trans. Comput.

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Abstract-Particle filters constitute themselves a highly powerful estimation tool, especially when dealing with non-linear non-Gaussian systems. However, traditional approaches present several limitations, which reduce significantly their performance. Evolutionary algorithms, and more specifically their optimization capabilities, may be used in order to overcome particle-filtering weaknesses. In this paper, a novel FPGA-based particle filter that takes advantage of evolutionary computation in order to estimate motion patterns is presented. The evolutionary algorithm, which has been included inside the resampling stage, mitigates the known sample impoverishment phenomenon, very common in particle-filtering systems. In addition, a hybrid mutation technique using two different mutation operators, each of them with a specific purpose, is proposed in order to enhance estimation results and make a more robust system. Moreover, implementing the proposed Evolutionary Particle Filter as a hardware accelerator has led to faster processing times than different software implementations of the same algorithm.

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“…Particularly interesting, in a PMC, {x k } k≥0 is not necessarily an MC [27], which is an hypothesis used in most filtering algorithms; and given (x 0:k , y 0:i−1 ) with 0 ≤ i ≤ k, the law of y i does not necessarily depend on x i only, but also on x i−1 , y i−1 and x i+1:k . Note that in a stationary reversible PMC, {x k } k≥0 is an MC if and only if p(y i |x 0:k ) = p(y i |x i ) [28].…”

Section: Pmc Modelsmentioning

confidence: 99%

“…Finally, (12) can either be computed exactly [29, eqs. (13.56) and (13.57)] [30] (in the linear and Gaussian case) or approximated via Monte Carlo approximations [27].…”

Section: Pmc Modelsmentioning

confidence: 99%

Bayesian Multi-Object Filtering for Pairwise Markov Chains

Petetin

Desbouvries

2013

IEEE Trans. Signal Process.

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Abstract-Random Finite Sets (RFS) are recent tools for addressing the multi-object filtering problem. The Probability Hypothesis Density (PHD) Filter is an approximation of the multiobject Bayesian filter which results from the RFS formulation of the problem and has been used in many applications. In the RFS framework, it is assumed that each target and associated observation follow a Hidden Markov Chain (HMC) model. HMCs conveniently describe some physical properties of practical interest for practitioners, but they also implicitly imply restrictive independence properties which, in practice, may not be satisfied by data. In this paper, we show that these structural limitations of HMC models can somehow be relaxed by embedding them into the more general class of Pairwise Markov Chain (PMC) models. We thus focus on the computation of the PHD filter in a PMC framework, and we propose a practical implementation of the PHD filter for a particular class of PMC models.

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Particle filtering with pairwise Markov processes

Cited by 7 publications

References 10 publications

Particle Filtering With Dependent Noise Processes

Particle Filtering With Dependent Noise Processes

Evolutionary Computing and Particle Filtering: A Hardware-Based Motion Estimation System

Bayesian Multi-Object Filtering for Pairwise Markov Chains

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