Robust target motion analysis using the possibility particle filter

Ristic, B, Houssineau, J and Arulampalam, S 2019, 'Robust target motion analysis using the possibility particle filter', IET Radar Sonar Navigation, vol. 13, no. 1, pp. 18-22.

Document type: Journal Article
Collection: Journal Articles

Title Robust target motion analysis using the possibility particle filter
Author(s) Ristic, B
Houssineau, J
Arulampalam, S
Year 2019
Journal name IET Radar Sonar Navigation
Volume number 13
Issue number 1
Start page 18
End page 22
Total pages 5
Publisher The Institution of Engineering and Technology
Abstract Bearings-only target motion analysis (TMA) is the process of estimating the state of a moving emitting target from noisy measurements collected by a single passive observer. The focus of this study is on recursive TMA, traditionally solved using the Bayesian filters (e.g. extended or unscented Kalman filters, particle filters). The TMA is a difficult problem and may result in track divergence, especially when the assumed probabilistic models are imperfect or mismatched. As a robust alternative to Bayesian filters for TMA, the authors present a recently proposed stochastic filter referred to as the possibility filter. The filter is implemented in the sequential Monte Carlo framework, and named the possibility particle filter. This study demonstrates its superior performance against the standard (Bayesian) particle filter in the presence of a model mismatch, while in the case of the exact model match, its performance equals that of the standard particle filter.
Subject Signal Processing
Keyword(s) Bayes methods
nonlinear filters
stochastic processes
particle filtering (numerical methods)
Monte Carlo methods
Kalman filters
target tracking
DOI - identifier 10.1049/iet-rsn.2018.5144
Copyright notice © The Institution of Engineering and Technology 2018
ISSN 1751-8792
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Citation counts: TR Web of Science Citation Count  Cited 4 times in Thomson Reuters Web of Science Article | Citations
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