Robust Model-Dependent Poisson Multi Bernoulli Mixture Trackers for Multistatic Sonar Networks
This work proposes a robust tracker based on the Poisson Multi Bernoulli Mixture (PMBM) filter for multistatic sonar networks (MSNs) systems. The PMBM based trackers estimate the number of targets and provide the target information via Bernoulli and Poisson Point Processes. The PMBM based trackers h...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9642980/ |
| _version_ | 1819320515079176192 |
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| author | Erhan Ozer Ali Koksal Hocaoglu |
| author_facet | Erhan Ozer Ali Koksal Hocaoglu |
| author_sort | Erhan Ozer |
| collection | DOAJ |
| description | This work proposes a robust tracker based on the Poisson Multi Bernoulli Mixture (PMBM) filter for multistatic sonar networks (MSNs) systems. The PMBM based trackers estimate the number of targets and provide the target information via Bernoulli and Poisson Point Processes. The PMBM based trackers handle existing tracks, undetected targets, and new births separately at each computation step by using these two processes together. In practice, the PMBM tracker aims to initiate the track as soon as possible and maintain the track continuity. Initiating track and maintaining track continuity are hard in challenging underwater environments without adapting the algorithm to changing environmental conditions. This paper uses the adaptive measurement-driven birth process and multistatic acoustic model-dependent probability of detection specifications. The adaptive measurement-driven birth process improves the robustness of the track initiation, and the multistatic acoustic model-dependent probability of detection advances the track continuity through the transition regions. These contributions to the PMBM tracker make it robust in terms of tracker performance in challenging underwater environments and acoustic transition regions where it is hard to get an accurate measurement. |
| first_indexed | 2024-12-24T11:20:48Z |
| format | Article |
| id | doaj.art-6273aeb45a0b40d3a9cd6196b3b2703e |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-24T11:20:48Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-6273aeb45a0b40d3a9cd6196b3b2703e2022-12-21T16:58:14ZengIEEEIEEE Access2169-35362021-01-01916361216362410.1109/ACCESS.2021.31341739642980Robust Model-Dependent Poisson Multi Bernoulli Mixture Trackers for Multistatic Sonar NetworksErhan Ozer0https://orcid.org/0000-0002-5648-1734Ali Koksal Hocaoglu1https://orcid.org/0000-0003-0701-2787Department of Electronics Engineering, Gebze Technical University, Kocaeli, TurkeyDepartment of Electronics Engineering, Gebze Technical University, Kocaeli, TurkeyThis work proposes a robust tracker based on the Poisson Multi Bernoulli Mixture (PMBM) filter for multistatic sonar networks (MSNs) systems. The PMBM based trackers estimate the number of targets and provide the target information via Bernoulli and Poisson Point Processes. The PMBM based trackers handle existing tracks, undetected targets, and new births separately at each computation step by using these two processes together. In practice, the PMBM tracker aims to initiate the track as soon as possible and maintain the track continuity. Initiating track and maintaining track continuity are hard in challenging underwater environments without adapting the algorithm to changing environmental conditions. This paper uses the adaptive measurement-driven birth process and multistatic acoustic model-dependent probability of detection specifications. The adaptive measurement-driven birth process improves the robustness of the track initiation, and the multistatic acoustic model-dependent probability of detection advances the track continuity through the transition regions. These contributions to the PMBM tracker make it robust in terms of tracker performance in challenging underwater environments and acoustic transition regions where it is hard to get an accurate measurement.https://ieeexplore.ieee.org/document/9642980/Multistatic sonar networksmultistatic sonar trackermultiple target trackingPoisson multi Bernoulli mixturerandom finite settrajectory tracker |
| spellingShingle | Erhan Ozer Ali Koksal Hocaoglu Robust Model-Dependent Poisson Multi Bernoulli Mixture Trackers for Multistatic Sonar Networks IEEE Access Multistatic sonar networks multistatic sonar tracker multiple target tracking Poisson multi Bernoulli mixture random finite set trajectory tracker |
| title | Robust Model-Dependent Poisson Multi Bernoulli Mixture Trackers for Multistatic Sonar Networks |
| title_full | Robust Model-Dependent Poisson Multi Bernoulli Mixture Trackers for Multistatic Sonar Networks |
| title_fullStr | Robust Model-Dependent Poisson Multi Bernoulli Mixture Trackers for Multistatic Sonar Networks |
| title_full_unstemmed | Robust Model-Dependent Poisson Multi Bernoulli Mixture Trackers for Multistatic Sonar Networks |
| title_short | Robust Model-Dependent Poisson Multi Bernoulli Mixture Trackers for Multistatic Sonar Networks |
| title_sort | robust model dependent poisson multi bernoulli mixture trackers for multistatic sonar networks |
| topic | Multistatic sonar networks multistatic sonar tracker multiple target tracking Poisson multi Bernoulli mixture random finite set trajectory tracker |
| url | https://ieeexplore.ieee.org/document/9642980/ |
| work_keys_str_mv | AT erhanozer robustmodeldependentpoissonmultibernoullimixturetrackersformultistaticsonarnetworks AT alikoksalhocaoglu robustmodeldependentpoissonmultibernoullimixturetrackersformultistaticsonarnetworks |