Track-Before-Detect Labeled Multi-Bernoulli Particle Filter With Label Switching

被引：34

作者：

Garcia-Fernandez, Angel F. ^{[1
]}

机构：

[1] Curtin Univ, Dept Elect & Comp Engn, GPO Box U1987, Perth, WA 6102, Australia

来源：

IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS | 2016年 / 52卷 / 05期

基金：

澳大利亚研究理事会;

关键词：

RANDOM FINITE SETS; MULTITARGET TRACKING; SOURCE LOCALIZATION; BAYESIAN-APPROACH; TARGET DETECTION; EFFICIENT;

D O I：

10.1109/TAES.2016.150343

中图分类号：

V [航空、航天];

学科分类号：

08 ; 0825 ;

摘要：

This paper presents a multitarget tracking particle filter for general track-before-detect measurement models. The particle filter is presented in the random-finite-set framework and uses a labeled multi-Bernoulli approximation. I also present a label-switching improvement algorithm based on Markov-chain Monte Carlo methods that is expected to increase filter performance if targets are in close proximity for a sufficiently long time. The particle filter is tested in two challenging numerical examples.

引用

页码：2123 / 2138

页数：16

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