An overview of particle methods for random finite set models

Ristic, B, Beard, M and Fantacci, C 2016, 'An overview of particle methods for random finite set models', Information Fusion, vol. 31, pp. 110-126.

Document type: Journal Article
Collection: Journal Articles

Title An overview of particle methods for random finite set models
Author(s) Ristic, B
Beard, M
Fantacci, C
Year 2016
Journal name Information Fusion
Volume number 31
Start page 110
End page 126
Total pages 17
Publisher Elsevier BV
Abstract This overview paper describes the particle methods developed for the implementation of the a class of Bayes filters formulated using the random finite set formalism. It is primarily intended for the readership already familiar with the particle methods in the context of the standard Bayes filter. The focus in on the Bernoulli particle filter, the probability hypothesis density (PHD) particle filter and the generalised labelled multi-Bernoulli (GLMB) particle filter. The performance of the described filters is demonstrated in the context of bearings-only target tracking application.
Subject Signal Processing
DOI - identifier 10.1016/j.inffus.2016.02.004
Copyright notice © 2016 Elsevier B.V. All rights reserved.
ISSN 1566-2535
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 10 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 8 times in Scopus Article | Citations
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