Track-Before-Detect Algorithm Based on Cost-Reference Particle Filter Bank for Weak Target Detection
Detecting weak target is an important and challenging problem in many applications such as radar, sonar etc. However, conventional detection methods are often ineffective in this case because of low signal-to-noise ratio (SNR). This paper presents a track-before-detect (TBD) algorithm based on an im...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10304144/ |
| _version_ | 1797633997284573184 |
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| author | Jin Lu Guojie Peng Weichuan Zhang Changming Sun |
| author_facet | Jin Lu Guojie Peng Weichuan Zhang Changming Sun |
| author_sort | Jin Lu |
| collection | DOAJ |
| description | Detecting weak target is an important and challenging problem in many applications such as radar, sonar etc. However, conventional detection methods are often ineffective in this case because of low signal-to-noise ratio (SNR). This paper presents a track-before-detect (TBD) algorithm based on an improved particle filter, i.e. cost-reference particle filter bank (CRPFB), which turns the problem of target detection to the problem of two-layer hypothesis testing. The first layer is implemented by CRPFB for state estimation of possible target. CRPFB has entirely parallel structure, consisting amounts of cost-reference particle filters with different hypothesized prior information. The second layer is to compare a test metric with a given threshold, which is constructed from the output of the first layer and fits GEV distribution. The performance of our proposed TBD algorithm and the existed TBD algorithms are compared according to the experiments on nonlinear frequency modulated (NLFM) signal detection and tracking. Simulation results show that the proposed TBD algorithm has better performance than the state-of-the-arts in detection, tracking, and time efficiency. |
| first_indexed | 2024-03-11T12:02:33Z |
| format | Article |
| id | doaj.art-4edf2b916c6e4851a37d63d4822f302d |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-11T12:02:33Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-4edf2b916c6e4851a37d63d4822f302d2023-11-08T00:01:01ZengIEEEIEEE Access2169-35362023-01-011112168812170110.1109/ACCESS.2023.332930010304144Track-Before-Detect Algorithm Based on Cost-Reference Particle Filter Bank for Weak Target DetectionJin Lu0https://orcid.org/0000-0003-2736-2549Guojie Peng1https://orcid.org/0009-0003-8853-7012Weichuan Zhang2https://orcid.org/0000-0003-4904-1826Changming Sun3https://orcid.org/0000-0001-5943-1989School of Electrical Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi’an, ChinaSchool of Electrical Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi’an, ChinaSchool of Electrical Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi’an, ChinaCSIRO Data61, Epping, NSW, AustraliaDetecting weak target is an important and challenging problem in many applications such as radar, sonar etc. However, conventional detection methods are often ineffective in this case because of low signal-to-noise ratio (SNR). This paper presents a track-before-detect (TBD) algorithm based on an improved particle filter, i.e. cost-reference particle filter bank (CRPFB), which turns the problem of target detection to the problem of two-layer hypothesis testing. The first layer is implemented by CRPFB for state estimation of possible target. CRPFB has entirely parallel structure, consisting amounts of cost-reference particle filters with different hypothesized prior information. The second layer is to compare a test metric with a given threshold, which is constructed from the output of the first layer and fits GEV distribution. The performance of our proposed TBD algorithm and the existed TBD algorithms are compared according to the experiments on nonlinear frequency modulated (NLFM) signal detection and tracking. Simulation results show that the proposed TBD algorithm has better performance than the state-of-the-arts in detection, tracking, and time efficiency.https://ieeexplore.ieee.org/document/10304144/Track-before-detectcost-reference particle filterfilter bankextreme value theory |
| spellingShingle | Jin Lu Guojie Peng Weichuan Zhang Changming Sun Track-Before-Detect Algorithm Based on Cost-Reference Particle Filter Bank for Weak Target Detection IEEE Access Track-before-detect cost-reference particle filter filter bank extreme value theory |
| title | Track-Before-Detect Algorithm Based on Cost-Reference Particle Filter Bank for Weak Target Detection |
| title_full | Track-Before-Detect Algorithm Based on Cost-Reference Particle Filter Bank for Weak Target Detection |
| title_fullStr | Track-Before-Detect Algorithm Based on Cost-Reference Particle Filter Bank for Weak Target Detection |
| title_full_unstemmed | Track-Before-Detect Algorithm Based on Cost-Reference Particle Filter Bank for Weak Target Detection |
| title_short | Track-Before-Detect Algorithm Based on Cost-Reference Particle Filter Bank for Weak Target Detection |
| title_sort | track before detect algorithm based on cost reference particle filter bank for weak target detection |
| topic | Track-before-detect cost-reference particle filter filter bank extreme value theory |
| url | https://ieeexplore.ieee.org/document/10304144/ |
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