Multiple Mobile Target Detection and Tracking in Small Active Sonar Array
Biodiversity monitoring requires the discovery of multi-target tracking. The main requirement is not to reduce the localization error but the continuity of the tracks: a high ratio between the duration of the track and the lifetime of the target. To this end, we present an algorithm for detecting an...
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MDPI AG
2025-06-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/11/1925 |
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| author | Avi Abu Nikola Mišković Neven Cukrov Roee Diamant |
| author_facet | Avi Abu Nikola Mišković Neven Cukrov Roee Diamant |
| author_sort | Avi Abu |
| collection | DOAJ |
| description | Biodiversity monitoring requires the discovery of multi-target tracking. The main requirement is not to reduce the localization error but the continuity of the tracks: a high ratio between the duration of the track and the lifetime of the target. To this end, we present an algorithm for detecting and tracking mobile underwater targets that utilizes reflections from active acoustic emission of broadband signals received by a rigid hydrophone array. The method overcomes the problem of a high false alarm rate by applying a tracking approach to the sequence of received reflections. A 2D time–distance matrix is created for the reflections received from each transmitted probe signal by performing delay and sum beamforming and pulse compression. The result is filtered by a 2D constant false alarm rate (CFAR) detector to identify reflection patterns that correspond to potential targets. Closely spaced signals for multiple probe transmissions are combined into blobs to avoid multiple detections of a single target. The position and velocity are estimated using the debiased converted measurement Kalman filter. The results are analyzed for simulated scenarios and for experiments in the Adriatic Sea, where six Global Positioning System (GPS)-tagged gilt-head seabream fish were released and tracked by a dedicated autonomous float system. Compared to four recent benchmark methods, the results show favorable tracking continuity and accuracy that is robust to the choice of detection threshold. |
| format | Article |
| id | doaj-art-63db081d0e2f448ab7fec1aa8a254c70 |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-63db081d0e2f448ab7fec1aa8a254c702025-08-20T03:11:20ZengMDPI AGRemote Sensing2072-42922025-06-011711192510.3390/rs17111925Multiple Mobile Target Detection and Tracking in Small Active Sonar ArrayAvi Abu0Nikola Mišković1Neven Cukrov2Roee Diamant3Hatter Department of Marine Technologies, University of Haifa, Haifa 3103301, IsraelFaculty of Electrical Engineering and Computing, University of Zagreb, 10000 Zagreb, CroatiaRuder Bošković Institute, 10000 Zagreb, CroatiaHatter Department of Marine Technologies, University of Haifa, Haifa 3103301, IsraelBiodiversity monitoring requires the discovery of multi-target tracking. The main requirement is not to reduce the localization error but the continuity of the tracks: a high ratio between the duration of the track and the lifetime of the target. To this end, we present an algorithm for detecting and tracking mobile underwater targets that utilizes reflections from active acoustic emission of broadband signals received by a rigid hydrophone array. The method overcomes the problem of a high false alarm rate by applying a tracking approach to the sequence of received reflections. A 2D time–distance matrix is created for the reflections received from each transmitted probe signal by performing delay and sum beamforming and pulse compression. The result is filtered by a 2D constant false alarm rate (CFAR) detector to identify reflection patterns that correspond to potential targets. Closely spaced signals for multiple probe transmissions are combined into blobs to avoid multiple detections of a single target. The position and velocity are estimated using the debiased converted measurement Kalman filter. The results are analyzed for simulated scenarios and for experiments in the Adriatic Sea, where six Global Positioning System (GPS)-tagged gilt-head seabream fish were released and tracked by a dedicated autonomous float system. Compared to four recent benchmark methods, the results show favorable tracking continuity and accuracy that is robust to the choice of detection threshold.https://www.mdpi.com/2072-4292/17/11/1925sonar imagesmulti-target trackingadaptive beamformingtrackingunderwater target detectionbiodiversity estimation |
| spellingShingle | Avi Abu Nikola Mišković Neven Cukrov Roee Diamant Multiple Mobile Target Detection and Tracking in Small Active Sonar Array Remote Sensing sonar images multi-target tracking adaptive beamforming tracking underwater target detection biodiversity estimation |
| title | Multiple Mobile Target Detection and Tracking in Small Active Sonar Array |
| title_full | Multiple Mobile Target Detection and Tracking in Small Active Sonar Array |
| title_fullStr | Multiple Mobile Target Detection and Tracking in Small Active Sonar Array |
| title_full_unstemmed | Multiple Mobile Target Detection and Tracking in Small Active Sonar Array |
| title_short | Multiple Mobile Target Detection and Tracking in Small Active Sonar Array |
| title_sort | multiple mobile target detection and tracking in small active sonar array |
| topic | sonar images multi-target tracking adaptive beamforming tracking underwater target detection biodiversity estimation |
| url | https://www.mdpi.com/2072-4292/17/11/1925 |
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