Labeled Multi-Bernoulli Filter Joint Detection and Tracking of Radar Targets

Labeled Multi-Bernoulli Filter Joint Detection and Tracking of Radar Targets

A labeled multi-Bernoulli (LMB) filter is presented to jointly detect and track radar targets. A relevant LMB filter is recently proposed by Rathnayake which assumes that the measurements of different targets do not overlap, leading to the favorable separable likelihood assumption. However, new or c...

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Main Authors: Rang Liu, Hongqi Fan, Huaitie Xiao
Format: Article
Language:English
Published: MDPI AG 2019-10-01
Series:Applied Sciences
Subjects:
multi-target tracking
random finite set
labeled multi-bernoulli
track before detect
Online Access:https://www.mdpi.com/2076-3417/9/19/4187
id doaj-59e2895bc8cd4714b54f45bb1dd44c9f
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spelling doaj-59e2895bc8cd4714b54f45bb1dd44c9f2020-11-25T02:29:35ZengMDPI AGApplied Sciences2076-34172019-10-01919418710.3390/app9194187app9194187Labeled Multi-Bernoulli Filter Joint Detection and Tracking of Radar TargetsRang Liu0Hongqi Fan1Huaitie Xiao2National Key Laboratory of Science and Technology on ATR, College of Electronic Science, National University of Defense Technology, Changsha 410073, ChinaNational Key Laboratory of Science and Technology on ATR, College of Electronic Science, National University of Defense Technology, Changsha 410073, ChinaNational Key Laboratory of Science and Technology on ATR, College of Electronic Science, National University of Defense Technology, Changsha 410073, ChinaA labeled multi-Bernoulli (LMB) filter is presented to jointly detect and track radar targets. A relevant LMB filter is recently proposed by Rathnayake which assumes that the measurements of different targets do not overlap, leading to the favorable separable likelihood assumption. However, new or close tracks often violate the assumption and lead to a bias in the cardinality estimate. To address this problem, a one-to-one association method between measurements and tracks is proposed. In our method, any target only corresponds to its associated measurements and different tracks have little mutual interference. In addition, an approximate method for calculating the point spread function of radar is developed to improve the computational efficiency of likelihood function. The simulation under low signal-to-noise ratio scenario with closely spaced targets have demonstrated the effectiveness and efficiency of the proposed algorithm.https://www.mdpi.com/2076-3417/9/19/4187multi-target trackingrandom finite setlabeled multi-bernoullitrack before detect
collection DOAJ
language English
format Article
sources DOAJ
author Rang Liu
Hongqi Fan
Huaitie Xiao
spellingShingle Rang Liu
Hongqi Fan
Huaitie Xiao
Labeled Multi-Bernoulli Filter Joint Detection and Tracking of Radar Targets
Applied Sciences
multi-target tracking
random finite set
labeled multi-bernoulli
track before detect
author_facet Rang Liu
Hongqi Fan
Huaitie Xiao
author_sort Rang Liu
title Labeled Multi-Bernoulli Filter Joint Detection and Tracking of Radar Targets
title_short Labeled Multi-Bernoulli Filter Joint Detection and Tracking of Radar Targets
title_full Labeled Multi-Bernoulli Filter Joint Detection and Tracking of Radar Targets
title_fullStr Labeled Multi-Bernoulli Filter Joint Detection and Tracking of Radar Targets
title_full_unstemmed Labeled Multi-Bernoulli Filter Joint Detection and Tracking of Radar Targets
title_sort labeled multi-bernoulli filter joint detection and tracking of radar targets
publisher MDPI AG
series Applied Sciences
issn 2076-3417
publishDate 2019-10-01
description A labeled multi-Bernoulli (LMB) filter is presented to jointly detect and track radar targets. A relevant LMB filter is recently proposed by Rathnayake which assumes that the measurements of different targets do not overlap, leading to the favorable separable likelihood assumption. However, new or close tracks often violate the assumption and lead to a bias in the cardinality estimate. To address this problem, a one-to-one association method between measurements and tracks is proposed. In our method, any target only corresponds to its associated measurements and different tracks have little mutual interference. In addition, an approximate method for calculating the point spread function of radar is developed to improve the computational efficiency of likelihood function. The simulation under low signal-to-noise ratio scenario with closely spaced targets have demonstrated the effectiveness and efficiency of the proposed algorithm.
topic multi-target tracking
random finite set
labeled multi-bernoulli
track before detect
url https://www.mdpi.com/2076-3417/9/19/4187
work_keys_str_mv AT rangliu labeledmultibernoullifilterjointdetectionandtrackingofradartargets
AT hongqifan labeledmultibernoullifilterjointdetectionandtrackingofradartargets
AT huaitiexiao labeledmultibernoullifilterjointdetectionandtrackingofradartargets
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