Arbitrary clutter extended target probability hypothesis density filter
Abstract Based on the random finite set (RFS) framework and the probability hypothesis density (PHD) filter, the extended target PHD (ET‐PHD) filter is proposed for multiple extended target tracking. However, the clutter process in the ET‐PHD filter is modelled as Poisson RFS, which is reasonable fo...
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Online Access: | https://doi.org/10.1049/rsn2.12041 |
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doaj-06a509923d8b48ef96cab4e17bb31d422021-08-02T08:20:21ZengWileyIET Radar, Sonar & Navigation1751-87841751-87922021-05-0115551052210.1049/rsn2.12041Arbitrary clutter extended target probability hypothesis density filterXinglin Shen0Luping Zhang1Moufa Hu2Shanzhu Xiao3Huamin Tao4National Key Laboratory of Science and Technology on Automatic Target Recognition College of Electronic Science National University of Defense Technology Changsha ChinaNational Key Laboratory of Science and Technology on Automatic Target Recognition College of Electronic Science National University of Defense Technology Changsha ChinaNational Key Laboratory of Science and Technology on Automatic Target Recognition College of Electronic Science National University of Defense Technology Changsha ChinaNational Key Laboratory of Science and Technology on Automatic Target Recognition College of Electronic Science National University of Defense Technology Changsha ChinaNational Key Laboratory of Science and Technology on Automatic Target Recognition College of Electronic Science National University of Defense Technology Changsha ChinaAbstract Based on the random finite set (RFS) framework and the probability hypothesis density (PHD) filter, the extended target PHD (ET‐PHD) filter is proposed for multiple extended target tracking. However, the clutter process in the ET‐PHD filter is modelled as Poisson RFS, which is reasonable for only some scenarios in reality. An easily implemented ET‐PHD filter for arbitrary clutter process, has not yet been studied. In this work, another form of the general PHD filter, suitable for arbitrary clutter and target measurement is derived using a probability generating functional (PGFL)‐based RFS framework. The proposed filter is equivalent to the general PHD filter proposed by Clark and Mahler, but it is easily implemented in an arbitrary clutter process because its clutter process is denoted by the probability density function . Then, an arbitrary clutter ET‐PHD (AC‐ET‐PHD) filter is simplified from the general PHD filter derived by us. To reduce the computational complexity of the proposed filter, a variant of the distance partitioning algorithm is put forward. Simulation results show that the AC‐ET‐PHD filter can be applied to the scenarios with the non‐Poisson clutter process, which means that it will be useful for multiple extended targets tracking in a more complicated clutter process.https://doi.org/10.1049/rsn2.12041 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xinglin Shen Luping Zhang Moufa Hu Shanzhu Xiao Huamin Tao |
spellingShingle |
Xinglin Shen Luping Zhang Moufa Hu Shanzhu Xiao Huamin Tao Arbitrary clutter extended target probability hypothesis density filter IET Radar, Sonar & Navigation |
author_facet |
Xinglin Shen Luping Zhang Moufa Hu Shanzhu Xiao Huamin Tao |
author_sort |
Xinglin Shen |
title |
Arbitrary clutter extended target probability hypothesis density filter |
title_short |
Arbitrary clutter extended target probability hypothesis density filter |
title_full |
Arbitrary clutter extended target probability hypothesis density filter |
title_fullStr |
Arbitrary clutter extended target probability hypothesis density filter |
title_full_unstemmed |
Arbitrary clutter extended target probability hypothesis density filter |
title_sort |
arbitrary clutter extended target probability hypothesis density filter |
publisher |
Wiley |
series |
IET Radar, Sonar & Navigation |
issn |
1751-8784 1751-8792 |
publishDate |
2021-05-01 |
description |
Abstract Based on the random finite set (RFS) framework and the probability hypothesis density (PHD) filter, the extended target PHD (ET‐PHD) filter is proposed for multiple extended target tracking. However, the clutter process in the ET‐PHD filter is modelled as Poisson RFS, which is reasonable for only some scenarios in reality. An easily implemented ET‐PHD filter for arbitrary clutter process, has not yet been studied. In this work, another form of the general PHD filter, suitable for arbitrary clutter and target measurement is derived using a probability generating functional (PGFL)‐based RFS framework. The proposed filter is equivalent to the general PHD filter proposed by Clark and Mahler, but it is easily implemented in an arbitrary clutter process because its clutter process is denoted by the probability density function . Then, an arbitrary clutter ET‐PHD (AC‐ET‐PHD) filter is simplified from the general PHD filter derived by us. To reduce the computational complexity of the proposed filter, a variant of the distance partitioning algorithm is put forward. Simulation results show that the AC‐ET‐PHD filter can be applied to the scenarios with the non‐Poisson clutter process, which means that it will be useful for multiple extended targets tracking in a more complicated clutter process. |
url |
https://doi.org/10.1049/rsn2.12041 |
work_keys_str_mv |
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1721238455396073472 |