Tracking multiple maneuvering targets using a sequential multiple target Bayes filter with jump Markov system models

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• 作者：Zong-xiang Liu ; ^{liuzx@szu.edu.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Qi-quan ZhangAuthor Vitae ; Liang-qun LiAuthor Vitae ; Wei-xin XieAuthor Vitae
• 关键词：Target tracking ; Jump Markov system models ; Bayes filter ; Sequential Bayes filter ; Maneuvering target
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：5 December 2016
• 年：2016
• 卷：216
• 期：Complete
• 页码：183-191
• 全文大小：939 K

文摘

Tracking multiple maneuvering (MM) targets is a well-known and challenging problem because of clutter and several uncertainties existing in target motion mode, target detection, and data association. An efficient solution to this problem is the Gaussian mixture probability hypothesis density (GM-PHD) filter for jump Markov system (JMS) models. However, this solution is inapplicable to circumstances where detection probability is low because the GM-PHD filter for JMS models requires a high detection probability. To address this problem, we propose a sequential multiple target (MT) Bayes filter for JMS models. To track MM targets that are switching among a set of linear Gaussian models, an implementation process of this filter for linear Gaussian jump Markov MT models is also developed. The conclusion that the novel filter is more efficient for tracking MM targets than the existing filter for JMS models in circumstances of low detection probability is validated by simulation results.