A Distributed Underwater Multi-Target Tracking Algorithm Based on Two-Layer Particle Filter
Underwater multi-target tracking is one of the key technologies for military missions, including patrol and combat in the crucial area. Since the underwater environment is complex and targets’ trajectories may intersect when they are in a dense area, it is challenging to guarantee the precision of o...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/11/4/858 |
| _version_ | 1797604798035394560 |
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| author | Kunhu Kou Bochen Li Lu Ding Lei Song |
| author_facet | Kunhu Kou Bochen Li Lu Ding Lei Song |
| author_sort | Kunhu Kou |
| collection | DOAJ |
| description | Underwater multi-target tracking is one of the key technologies for military missions, including patrol and combat in the crucial area. Since the underwater environment is complex and targets’ trajectories may intersect when they are in a dense area, it is challenging to guarantee the precision of observed information. In order to provide high-precision underwater localization and tracking services over an underwater monitoring network, a dynamic network resource allocation mechanism and an underwater multi-target tracking algorithm based on a two-layer particle filter with distributed probability fusion (TLPF-DPF) are proposed. The position estimation model based on geometric constraints and the dynamic allocation mechanism of network resources based on prior position estimation are designed. Using the improved filtering algorithm with known initial states, the reliable tracking of multiple targets with trajectory intersection in a small area under complex noises is achieved. In the non-Gaussian environment, the average positioning error of TLPF-DPF is less by nearly 30% than alternative algorithms. When switching from a Gaussian environment to a non-Gaussian environment, the performance degradation of TLPF-DPF is less than 12%, which exhibits stability compared with other algorithms when targets are close to each other with crossing trajectories. |
| first_indexed | 2024-03-11T04:51:51Z |
| format | Article |
| id | doaj.art-43de0995f7214a0297e8f202b2e5083b |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-11T04:51:51Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-43de0995f7214a0297e8f202b2e5083b2023-11-17T19:57:03ZengMDPI AGJournal of Marine Science and Engineering2077-13122023-04-0111485810.3390/jmse11040858A Distributed Underwater Multi-Target Tracking Algorithm Based on Two-Layer Particle FilterKunhu Kou0Bochen Li1Lu Ding2Lei Song3Aeronautical Operations College, Naval Aviation University, Yantai 264001, ChinaDepartment of Automation, Shanghai Jiao Tong University, Shanghai 200240, ChinaSchool of Electrical Engineering, Guangxi University, Nanning 530004, ChinaDepartment of Automation, Shanghai Jiao Tong University, Shanghai 200240, ChinaUnderwater multi-target tracking is one of the key technologies for military missions, including patrol and combat in the crucial area. Since the underwater environment is complex and targets’ trajectories may intersect when they are in a dense area, it is challenging to guarantee the precision of observed information. In order to provide high-precision underwater localization and tracking services over an underwater monitoring network, a dynamic network resource allocation mechanism and an underwater multi-target tracking algorithm based on a two-layer particle filter with distributed probability fusion (TLPF-DPF) are proposed. The position estimation model based on geometric constraints and the dynamic allocation mechanism of network resources based on prior position estimation are designed. Using the improved filtering algorithm with known initial states, the reliable tracking of multiple targets with trajectory intersection in a small area under complex noises is achieved. In the non-Gaussian environment, the average positioning error of TLPF-DPF is less by nearly 30% than alternative algorithms. When switching from a Gaussian environment to a non-Gaussian environment, the performance degradation of TLPF-DPF is less than 12%, which exhibits stability compared with other algorithms when targets are close to each other with crossing trajectories.https://www.mdpi.com/2077-1312/11/4/858underwater monitoring networkcomplex noisemulti-target trackingparticle filter |
| spellingShingle | Kunhu Kou Bochen Li Lu Ding Lei Song A Distributed Underwater Multi-Target Tracking Algorithm Based on Two-Layer Particle Filter Journal of Marine Science and Engineering underwater monitoring network complex noise multi-target tracking particle filter |
| title | A Distributed Underwater Multi-Target Tracking Algorithm Based on Two-Layer Particle Filter |
| title_full | A Distributed Underwater Multi-Target Tracking Algorithm Based on Two-Layer Particle Filter |
| title_fullStr | A Distributed Underwater Multi-Target Tracking Algorithm Based on Two-Layer Particle Filter |
| title_full_unstemmed | A Distributed Underwater Multi-Target Tracking Algorithm Based on Two-Layer Particle Filter |
| title_short | A Distributed Underwater Multi-Target Tracking Algorithm Based on Two-Layer Particle Filter |
| title_sort | distributed underwater multi target tracking algorithm based on two layer particle filter |
| topic | underwater monitoring network complex noise multi-target tracking particle filter |
| url | https://www.mdpi.com/2077-1312/11/4/858 |
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