Direct versus prediction-based particle filter algorithms

François Desbouvries; Boujemaa Ait-El-Fquih; IEEE

doi:10.1109/MLSP.2008.4685497

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Conference proceeding

Direct versus prediction-based particle filter algorithms

François Desbouvries, Boujemaa Ait-El-Fquih and IEEE

Proceedings MLSP 2008 : IEEE International Workshop on Machine Learning for Signal Processing, pp.303-308

MLSP 2008 : IEEE International Workshop on Machine Learning for Signal Processing

2008

DOI: https://doi.org/10.1109/MLSP.2008.4685497

Abstract

Engineering Sciences

Signal and Image processing

Particle Filtering (PF) algorithms propagate in time a Monte Carlo (MC) approximation of the a posteriori filtering measure in a Hidden Markov Chain (HMC) model. In this paper we first shed some new light on two classical PF algorithms, which can be considered as natural MC implementations of two two-step direct recursive formulas for computing the filtering distribution. We next address the Particle Prediction (PP) problem, which happens to be simpler than the PF problem because the optimal prediction conditional importance distribution (CID) is much easier to sample from. Motivated by this result we finally develop two PP-based PF algorithms, and we compare our algorithms via simulations

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Details

Title: Direct versus prediction-based particle filter algorithms
Creators - without role: François Desbouvries - Traitement de l'Information Pour Images et Communications
Boujemaa Ait-El-Fquih - Laboratoire des signaux et systèmes
IEEE
Publication Details: Proceedings MLSP 2008 : IEEE International Workshop on Machine Learning for Signal Processing, pp.303-308
Conference: MLSP 2008 : IEEE International Workshop on Machine Learning for Signal Processing
Publisher: IEEE
Identifiers: 9942919208331
Academic Unit: King Abdullah University of Science & Technology
Language: English
Resource Type: Conference proceeding