Open AccessRao–Blackwellised particle filtering and smoothing for jump Markov non‐linear systems with mode observation
Author(s) -
Li Wenling,
Jia Yingmin
Publication year - 2013
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
ISSN - 1751-9683
DOI - 10.1049/iet-spr.2013.0023
Subject(s) - smoothing , particle filter , gaussian , algorithm , filtering problem , kalman filter , computer science , mode (computer interface) , tracking (education) , mathematics , markov chain , hidden markov model , mathematical optimization , markov process , filter (signal processing) , artificial intelligence , statistics , computer vision , extended kalman filter , psychology , pedagogy , physics , quantum mechanics , operating system
This study is concerned with the problem of filtering and fixed‐lag smoothing for jump Markov non‐linear systems when the mode information can be extracted from an image sensor. Based on the idea of Rao–Blackwellisation, the authors present a general theoretical framework to derive the recursive estimates by employing the particle filtering method. A suboptimal image‐enhanced Rao–Blackwellised particle filter is proposed, in which the mode state is estimated by using random sampling and the continuous state as well as the relevant likelihood function are approximated as Gaussian distributions. The one‐step fixed‐lag smoothing result is also obtained for such systems with lagged mode observations. Performance comparison of the proposed algorithms with the existing methods is provided through a manoeuvring target tracking simulation study.