Efficient joint probabilistic data association filter based on Kullback–Leibler divergence for multi‐target tracking-Reference-Cited by-同舟云学术

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Efficient joint probabilistic data association filter based on Kullback–Leibler divergence for multi‐target tracking

Published:2017-08-04 Issue:10 Volume:11 Page:1540-1548
ISSN:1751-8792
Container-title:IET Radar, Sonar & Navigation
language:en
Short-container-title:IET Radar, Sonar & Navigation

Author:

Zhu Yun¹,Wang Jun¹,Liang Shuang²

Affiliation:

1. National Laboratory of Radar Signal ProcessingXidian UniversityXi'an710071People's Republic of China

2. Key Laboratory of Intelligent Perception and Image Understanding of Ministry of EducationXidian UniversityXi'an710071People's Republic of China

Funder

National Natural Science Foundation of China

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/iet-rsn.2017.0102

Reference30 articles.

1. Sonar tracking of multiple targets using joint probabilistic data association

2. JIPDA*: automatic target tracking avoiding track coalescence;Blom H.;IEEE Trans. Aerosp. Electron. Syst.,2015

3. Multitarget range-azimuth tracker

4. Approaches to Cartesian Data Association Passive Radar Tracking in a DAB/DVB Network

5. Iterative joint integrated probabilistic data association for multitarget tracking

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