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Multi-Sonar Distributed Fusion for Target Detection and Tracking in Marine Environment
The multi-sonar distributed fusion system has been pervasively deployed to jointly detect and track marine targets. In the realistic scenario, the origin of locally transmitted tracks is uncertain due to clutter disturbance and the presence of multi-target. Moreover, attributed to the different sona...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9101747/ https://www.ncbi.nlm.nih.gov/pubmed/35591024 http://dx.doi.org/10.3390/s22093335 |
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author | Chen, Roujie Li, Tingting Memon, Imran Shi, Yifang Ullah, Ihsan Memon, Sufyan Ali |
author_facet | Chen, Roujie Li, Tingting Memon, Imran Shi, Yifang Ullah, Ihsan Memon, Sufyan Ali |
author_sort | Chen, Roujie |
collection | PubMed |
description | The multi-sonar distributed fusion system has been pervasively deployed to jointly detect and track marine targets. In the realistic scenario, the origin of locally transmitted tracks is uncertain due to clutter disturbance and the presence of multi-target. Moreover, attributed to the different sonar internal processing times and diverse communication delays between sonar and the fusion center, tracks unavoidably arrive in the fusion center with temporal out-of-sequence (OOS), both problems pose significant challenges to the fusion system. Under the distributed fusion framework with memory, this paper proposes a novel multiple forward prediction-integrated equivalent measurement fusion (MFP-IEMF) method, it fuses the multi-lag OOST with track origin uncertainty in an optimal manner and is capable to be implemented in both the synchronous and asynchronous multi-sonar tracks fusion system. Furthermore, a random central track initialization technique is also proposed to detect the randomly born marine target in time via quickly initiating and confirming true tracks. The numerical results show that the proposed algorithm achieves the same optimality as the existing OOS reprocessing method, and delivers substantially improved detection and tracking performance in terms of both ANCTT and estimation accuracy compared to the existing OOST discarding fusion method and the ANF-IFPFD method. |
format | Online Article Text |
id | pubmed-9101747 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-91017472022-05-14 Multi-Sonar Distributed Fusion for Target Detection and Tracking in Marine Environment Chen, Roujie Li, Tingting Memon, Imran Shi, Yifang Ullah, Ihsan Memon, Sufyan Ali Sensors (Basel) Article The multi-sonar distributed fusion system has been pervasively deployed to jointly detect and track marine targets. In the realistic scenario, the origin of locally transmitted tracks is uncertain due to clutter disturbance and the presence of multi-target. Moreover, attributed to the different sonar internal processing times and diverse communication delays between sonar and the fusion center, tracks unavoidably arrive in the fusion center with temporal out-of-sequence (OOS), both problems pose significant challenges to the fusion system. Under the distributed fusion framework with memory, this paper proposes a novel multiple forward prediction-integrated equivalent measurement fusion (MFP-IEMF) method, it fuses the multi-lag OOST with track origin uncertainty in an optimal manner and is capable to be implemented in both the synchronous and asynchronous multi-sonar tracks fusion system. Furthermore, a random central track initialization technique is also proposed to detect the randomly born marine target in time via quickly initiating and confirming true tracks. The numerical results show that the proposed algorithm achieves the same optimality as the existing OOS reprocessing method, and delivers substantially improved detection and tracking performance in terms of both ANCTT and estimation accuracy compared to the existing OOST discarding fusion method and the ANF-IFPFD method. MDPI 2022-04-27 /pmc/articles/PMC9101747/ /pubmed/35591024 http://dx.doi.org/10.3390/s22093335 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Chen, Roujie Li, Tingting Memon, Imran Shi, Yifang Ullah, Ihsan Memon, Sufyan Ali Multi-Sonar Distributed Fusion for Target Detection and Tracking in Marine Environment |
title | Multi-Sonar Distributed Fusion for Target Detection and Tracking in Marine Environment |
title_full | Multi-Sonar Distributed Fusion for Target Detection and Tracking in Marine Environment |
title_fullStr | Multi-Sonar Distributed Fusion for Target Detection and Tracking in Marine Environment |
title_full_unstemmed | Multi-Sonar Distributed Fusion for Target Detection and Tracking in Marine Environment |
title_short | Multi-Sonar Distributed Fusion for Target Detection and Tracking in Marine Environment |
title_sort | multi-sonar distributed fusion for target detection and tracking in marine environment |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9101747/ https://www.ncbi.nlm.nih.gov/pubmed/35591024 http://dx.doi.org/10.3390/s22093335 |
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