An efficient multiple particle filter based on the variational Bayesian approach

Ait‐El‐Fquih, Boujemaa; Hoteit, Ibrahim

doi:10.1109/isspit.2015.7394338

Cited by 5 publications

(1 citation statement)

References 24 publications

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“…Table IV outlines the standard deviation of the state estimates as suggested by the VBMPF and the MPF with different numbers of particles and different dimensions of state partitions. The ratios, CMPF CVBMPF , are also given to compare between the computational complexities of these algorithms; these are computed based on (40), (41), C f = 3n x + 8 and C h = 2n x . One can see that almost all errors in Table IV are much larger than those of Tables II and III.…”

Section: B the Case Of R Non-diagonalmentioning

confidence: 99%

A Variational Bayesian Multiple Particle Filtering Scheme for Large-Dimensional Systems

Ait‐El‐Fquih

Hoteit

2016

IEEE Trans. Signal Process.

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This paper considers the Bayesian filtering problem in high-dimensional nonlinear state-space systems. In such systems, classical particle filters (PFs) are impractical due to the prohibitive number of required particles to obtain reasonable performances. One approach that has been introduced to overcome this problem is the concept of multiple PFs (MPFs), where the state-space is split into low-dimensional subspaces and then a separate PF is applied to each subspace. Remarkable performances of MPF-like filters motivated our investigation here into a new strategy that combines the variational Bayesian approach to split the state-space with random sampling techniques, to derive a new computationally efficient MPF. The propagation of each particle in the prediction step of the resulting filter requires generating only a single particle in contrast with standard MPFs, for which a set of (children) particles is required. We present simulation results to evaluate the behavior of the proposed filter and compare its performances against standard PF and a MPF.

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Section: B the Case Of R Non-diagonalmentioning

confidence: 99%