Particle filters with independent resampling

In many signal processing applications we aim to track a state of interest given available observations. Among existing techniques, sequential Monte Carlo filters are importance sampling-based algorithms meant to propagate in time a set of weighted particles which represent the a posteriori density...

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Published in2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 3994 - 3998
Main Authors Lamberti, Roland, Petetin, Yohan, Septier, Francois, Desbouvries, Francois
Format Conference Proceeding Journal Article
LanguageEnglish
Published IEEE 01.03.2016
Subjects
Approximation algorithms
Atmospheric measurements
Conferences
Electronics
Filtering
Filtration
Hidden Markov models
Indexes
Monte Carlo methods
Particle filters
Particle measurements
Resampling
resampling procedures
Sequential Monte Carlo
Signal processing
Speech
Tracking
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Summary:In many signal processing applications we aim to track a state of interest given available observations. Among existing techniques, sequential Monte Carlo filters are importance sampling-based algorithms meant to propagate in time a set of weighted particles which represent the a posteriori density of interest. As is well known weights tend to degenerate over time, and resampling is a commonly used rescue for discarding particles with low weight. Unfortunately conditionally independent resampling produces a set of dependent samples and the technique suffers from sample impoverishment. In this paper we modify the resampling step of particle filtering techniques in order to produce independent samples per iteration. We validate our technique via simulations.
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SourceType-Conference Papers & Proceedings-2
ISSN:2379-190X
DOI:10.1109/ICASSP.2016.7472427