DA-PMHT for Multistatic Passive Radar Multitarget Tracking in Dense Clutter Environment

Multistatic passive radar exploits illuminators of opportunity such as radio or television stations for tracking airborne targets. This paper focuses on broadcast signals in a single frequency network modulated according to the digital audio/video broadcasting (DAB/DVB) standards. The main challenge...

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Main Authors: Xiaohua Li, Chenxu Zhao, Xiaofeng Lu, Wei Wei
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/8675294/
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author Xiaohua Li
Chenxu Zhao
Xiaofeng Lu
Wei Wei
author_facet Xiaohua Li
Chenxu Zhao
Xiaofeng Lu
Wei Wei
author_sort Xiaohua Li
collection DOAJ
description Multistatic passive radar exploits illuminators of opportunity such as radio or television stations for tracking airborne targets. This paper focuses on broadcast signals in a single frequency network modulated according to the digital audio/video broadcasting (DAB/DVB) standards. The main challenge is that one has to solve the measurement-to-illuminator association ambiguity in addition to the conventional measurement-to-target association ambiguity, which increases data association complexity greatly. In this paper, to solve the additional measurement-to-illuminator data association ambiguity, a novel deterministic annealing probabilistic multi-hypothesis tracker based on the extended Kalman filter is proposed. The proposed algorithm works directly in the original three-dimensional Cartesian space, and can efficiently handle a large number of association hypotheses among the targets, measurements, and illuminators in a natural way, also it is computationally attractive. As the angular measurement is not available or often of extremely poor quality, this paper assumes measurements of bistatic range and range rate and does not process bearing information. The simulation results show that the proposed algorithm works reasonably well in dense clutter environment for multistatic passive radar system.
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spelling doaj.art-756c9de0e2c84562a62b46f78416a3ec2022-12-21T23:02:42ZengIEEEIEEE Access2169-35362019-01-017493164932610.1109/ACCESS.2019.29077898675294DA-PMHT for Multistatic Passive Radar Multitarget Tracking in Dense Clutter EnvironmentXiaohua Li0https://orcid.org/0000-0002-3631-5552Chenxu Zhao1https://orcid.org/0000-0003-0832-3106Xiaofeng Lu2Wei Wei3https://orcid.org/0000-0001-5149-6542Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi’an University of Technology, Xi’an, ChinaScience and Technology on Integrated Logistics Support Laboratory, National University of Defense Technology, Changsha, ChinaShaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi’an University of Technology, Xi’an, ChinaShaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi’an University of Technology, Xi’an, ChinaMultistatic passive radar exploits illuminators of opportunity such as radio or television stations for tracking airborne targets. This paper focuses on broadcast signals in a single frequency network modulated according to the digital audio/video broadcasting (DAB/DVB) standards. The main challenge is that one has to solve the measurement-to-illuminator association ambiguity in addition to the conventional measurement-to-target association ambiguity, which increases data association complexity greatly. In this paper, to solve the additional measurement-to-illuminator data association ambiguity, a novel deterministic annealing probabilistic multi-hypothesis tracker based on the extended Kalman filter is proposed. The proposed algorithm works directly in the original three-dimensional Cartesian space, and can efficiently handle a large number of association hypotheses among the targets, measurements, and illuminators in a natural way, also it is computationally attractive. As the angular measurement is not available or often of extremely poor quality, this paper assumes measurements of bistatic range and range rate and does not process bearing information. The simulation results show that the proposed algorithm works reasonably well in dense clutter environment for multistatic passive radar system.https://ieeexplore.ieee.org/document/8675294/Multistatic passive radarextended Kalman filterdata associationdeterministic annealingprobabilistic multi-hypothesis tracker
spellingShingle Xiaohua Li
Chenxu Zhao
Xiaofeng Lu
Wei Wei
DA-PMHT for Multistatic Passive Radar Multitarget Tracking in Dense Clutter Environment
IEEE Access
Multistatic passive radar
extended Kalman filter
data association
deterministic annealing
probabilistic multi-hypothesis tracker
title DA-PMHT for Multistatic Passive Radar Multitarget Tracking in Dense Clutter Environment
title_full DA-PMHT for Multistatic Passive Radar Multitarget Tracking in Dense Clutter Environment
title_fullStr DA-PMHT for Multistatic Passive Radar Multitarget Tracking in Dense Clutter Environment
title_full_unstemmed DA-PMHT for Multistatic Passive Radar Multitarget Tracking in Dense Clutter Environment
title_short DA-PMHT for Multistatic Passive Radar Multitarget Tracking in Dense Clutter Environment
title_sort da pmht for multistatic passive radar multitarget tracking in dense clutter environment
topic Multistatic passive radar
extended Kalman filter
data association
deterministic annealing
probabilistic multi-hypothesis tracker
url https://ieeexplore.ieee.org/document/8675294/
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