Cyclic Peak Extraction from a Spatial Likelihood Map for Multi-Array Multi-Target Bearing-Only Localization
In the context of multi-array multi-target bearing-only localization, due to the existence of direction-finding errors, the crossing results of bearing lines cannot accurately determine correspondence with targets. Under conditions that clutter interference and missing of detection in direction-find...
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| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/1/109 |
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| author | Chuanxing Hu Bo Zhang Xishan Yang Zhaokai Zhai Dai Liu |
| author_facet | Chuanxing Hu Bo Zhang Xishan Yang Zhaokai Zhai Dai Liu |
| author_sort | Chuanxing Hu |
| collection | DOAJ |
| description | In the context of multi-array multi-target bearing-only localization, due to the existence of direction-finding errors, the crossing results of bearing lines cannot accurately determine correspondence with targets. Under conditions that clutter interference and missing of detection in direction-finding, the traditional method will produce false alarm targets and miss some targets. To address this issue, this paper draws on the idea of a spatial likelihood map which calculates the likelihood of target presence at each grid point within the observation area by partitioning the observation area into grids and utilizing bearing data from each array, yielding the distribution of targets in the observation area. Then, a multi-target cyclic peak extraction algorithm based on a statistical dual-threshold is proposed, which eliminates false peaks by cyclic extraction of target positions, so as to reduce false targets. Simulation results demonstrate that the spatial likelihood mapping-based localization exhibits good performance. Furthermore, when the multi-target cyclic peak extraction algorithm based on statistical dual-thresholds is applied, it outperforms direct target extraction from the spatial likelihood map, showcasing enhanced multi-target localization capabilities. Moreover, compared to the position non-linear least squares multi-target localization method, the proposed method has lower optimal sub-pattern assignment distance and lower localization error under the condition of interference and missing detection. |
| format | Article |
| id | doaj-art-4b0e85f79def410abd1deebc55c1e33e |
| institution | Kabale University |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-4b0e85f79def410abd1deebc55c1e33e2025-01-24T13:36:53ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-01-0113110910.3390/jmse13010109Cyclic Peak Extraction from a Spatial Likelihood Map for Multi-Array Multi-Target Bearing-Only LocalizationChuanxing Hu0Bo Zhang1Xishan Yang2Zhaokai Zhai3Dai Liu4State Key Laboratory of Acoustics, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, ChinaState Key Laboratory of Acoustics, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, ChinaState Key Laboratory of Acoustics, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, ChinaState Key Laboratory of Acoustics, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, ChinaState Key Laboratory of Acoustics, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, ChinaIn the context of multi-array multi-target bearing-only localization, due to the existence of direction-finding errors, the crossing results of bearing lines cannot accurately determine correspondence with targets. Under conditions that clutter interference and missing of detection in direction-finding, the traditional method will produce false alarm targets and miss some targets. To address this issue, this paper draws on the idea of a spatial likelihood map which calculates the likelihood of target presence at each grid point within the observation area by partitioning the observation area into grids and utilizing bearing data from each array, yielding the distribution of targets in the observation area. Then, a multi-target cyclic peak extraction algorithm based on a statistical dual-threshold is proposed, which eliminates false peaks by cyclic extraction of target positions, so as to reduce false targets. Simulation results demonstrate that the spatial likelihood mapping-based localization exhibits good performance. Furthermore, when the multi-target cyclic peak extraction algorithm based on statistical dual-thresholds is applied, it outperforms direct target extraction from the spatial likelihood map, showcasing enhanced multi-target localization capabilities. Moreover, compared to the position non-linear least squares multi-target localization method, the proposed method has lower optimal sub-pattern assignment distance and lower localization error under the condition of interference and missing detection.https://www.mdpi.com/2077-1312/13/1/109bearing intersectionbearing-only localizationcyclic extractionmulti-target localization |
| spellingShingle | Chuanxing Hu Bo Zhang Xishan Yang Zhaokai Zhai Dai Liu Cyclic Peak Extraction from a Spatial Likelihood Map for Multi-Array Multi-Target Bearing-Only Localization Journal of Marine Science and Engineering bearing intersection bearing-only localization cyclic extraction multi-target localization |
| title | Cyclic Peak Extraction from a Spatial Likelihood Map for Multi-Array Multi-Target Bearing-Only Localization |
| title_full | Cyclic Peak Extraction from a Spatial Likelihood Map for Multi-Array Multi-Target Bearing-Only Localization |
| title_fullStr | Cyclic Peak Extraction from a Spatial Likelihood Map for Multi-Array Multi-Target Bearing-Only Localization |
| title_full_unstemmed | Cyclic Peak Extraction from a Spatial Likelihood Map for Multi-Array Multi-Target Bearing-Only Localization |
| title_short | Cyclic Peak Extraction from a Spatial Likelihood Map for Multi-Array Multi-Target Bearing-Only Localization |
| title_sort | cyclic peak extraction from a spatial likelihood map for multi array multi target bearing only localization |
| topic | bearing intersection bearing-only localization cyclic extraction multi-target localization |
| url | https://www.mdpi.com/2077-1312/13/1/109 |
| work_keys_str_mv | AT chuanxinghu cyclicpeakextractionfromaspatiallikelihoodmapformultiarraymultitargetbearingonlylocalization AT bozhang cyclicpeakextractionfromaspatiallikelihoodmapformultiarraymultitargetbearingonlylocalization AT xishanyang cyclicpeakextractionfromaspatiallikelihoodmapformultiarraymultitargetbearingonlylocalization AT zhaokaizhai cyclicpeakextractionfromaspatiallikelihoodmapformultiarraymultitargetbearingonlylocalization AT dailiu cyclicpeakextractionfromaspatiallikelihoodmapformultiarraymultitargetbearingonlylocalization |