Particle filters with independent resampling

• Published in: 2016 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
• Language: English
• 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
• DOI: 10.1109/ICASSP.2016.7472427

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.