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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Main Authors: Chuanxing Hu, Bo Zhang, Xishan Yang, Zhaokai Zhai, Dai Liu
Format: Article
Language:English
Published: MDPI AG 2025-01-01
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.
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publishDate 2025-01-01
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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