A probability hypothesis density filter for tracking non‐rigid extended targets using spatiotemporal Gaussian process model
Abstract This paper proposes a random finite set (RFS)‐based algorithm to deal with the tracking problem of multiple non‐rigid extended targets (MNRET) with irregular shapes in the presence of clutter, false alarms and missed detection. The extensions of targets are modelled by spatiotemporal Gaussi...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi-IET
2022-12-01
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| Series: | IET Signal Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/sil2.12158 |
| _version_ | 1797423231387303936 |
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| author | Sunyong Wu Yusong Zhou Yun Xie Qiutiao Xue |
| author_facet | Sunyong Wu Yusong Zhou Yun Xie Qiutiao Xue |
| author_sort | Sunyong Wu |
| collection | DOAJ |
| description | Abstract This paper proposes a random finite set (RFS)‐based algorithm to deal with the tracking problem of multiple non‐rigid extended targets (MNRET) with irregular shapes in the presence of clutter, false alarms and missed detection. The extensions of targets are modelled by spatiotemporal Gaussian process, which is augmented with internal reference point (IRP) modelling the kinematics to construct the state of MNRET. The probability hypothesis density (PHD) filter is employed to propagate the first‐order moment of the RFS of MNRET. A suitable predicted likelihood of MNRET for the optimal partition is given, and the filter recursion is presented along with the necessary approximations and assumptions. More importantly, the closed‐form implementation and its corresponding smoothing filter are derived by converting the posterior density to Gaussian mixture form. Simulation results show the robustness and effectiveness of the proposed algorithm. |
| first_indexed | 2024-03-09T07:43:25Z |
| format | Article |
| id | doaj.art-92859451ccac4ccda8ddaeed3e108886 |
| institution | Directory Open Access Journal |
| issn | 1751-9675 1751-9683 |
| language | English |
| last_indexed | 2024-03-09T07:43:25Z |
| publishDate | 2022-12-01 |
| publisher | Hindawi-IET |
| record_format | Article |
| series | IET Signal Processing |
| spelling | doaj.art-92859451ccac4ccda8ddaeed3e1088862023-12-03T04:16:24ZengHindawi-IETIET Signal Processing1751-96751751-96832022-12-011691130114310.1049/sil2.12158A probability hypothesis density filter for tracking non‐rigid extended targets using spatiotemporal Gaussian process modelSunyong Wu0Yusong Zhou1Yun Xie2Qiutiao Xue3School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin ChinaSchool of Mathematics and Computing Science Guilin University of Electronic Technology Guilin ChinaSchool of Mathematics and Computing Science Guilin University of Electronic Technology Guilin ChinaSchool of Mathematics and Computing Science Guilin University of Electronic Technology Guilin ChinaAbstract This paper proposes a random finite set (RFS)‐based algorithm to deal with the tracking problem of multiple non‐rigid extended targets (MNRET) with irregular shapes in the presence of clutter, false alarms and missed detection. The extensions of targets are modelled by spatiotemporal Gaussian process, which is augmented with internal reference point (IRP) modelling the kinematics to construct the state of MNRET. The probability hypothesis density (PHD) filter is employed to propagate the first‐order moment of the RFS of MNRET. A suitable predicted likelihood of MNRET for the optimal partition is given, and the filter recursion is presented along with the necessary approximations and assumptions. More importantly, the closed‐form implementation and its corresponding smoothing filter are derived by converting the posterior density to Gaussian mixture form. Simulation results show the robustness and effectiveness of the proposed algorithm.https://doi.org/10.1049/sil2.12158Gaussian mixturenon‐rigid extended targetspredicted likelihoodprobability hypothesis densityrandom finite setSpatiotemporal Gaussian process |
| spellingShingle | Sunyong Wu Yusong Zhou Yun Xie Qiutiao Xue A probability hypothesis density filter for tracking non‐rigid extended targets using spatiotemporal Gaussian process model IET Signal Processing Gaussian mixture non‐rigid extended targets predicted likelihood probability hypothesis density random finite set Spatiotemporal Gaussian process |
| title | A probability hypothesis density filter for tracking non‐rigid extended targets using spatiotemporal Gaussian process model |
| title_full | A probability hypothesis density filter for tracking non‐rigid extended targets using spatiotemporal Gaussian process model |
| title_fullStr | A probability hypothesis density filter for tracking non‐rigid extended targets using spatiotemporal Gaussian process model |
| title_full_unstemmed | A probability hypothesis density filter for tracking non‐rigid extended targets using spatiotemporal Gaussian process model |
| title_short | A probability hypothesis density filter for tracking non‐rigid extended targets using spatiotemporal Gaussian process model |
| title_sort | probability hypothesis density filter for tracking non rigid extended targets using spatiotemporal gaussian process model |
| topic | Gaussian mixture non‐rigid extended targets predicted likelihood probability hypothesis density random finite set Spatiotemporal Gaussian process |
| url | https://doi.org/10.1049/sil2.12158 |
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