Bayesian information fusion and multitarget tracking for maritime situational awareness
© The Institution of Engineering and Technology 2020. The goal of maritime situational awareness (MSA) is to provide a seamless wide-area operational picture of ship traffic in coastal areas and the oceans in real time. Radar is a central sensing modality for MSA. In particular, oceanographic high-f...
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| Format: | Article |
| Language: | English |
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Institution of Engineering and Technology (IET)
2021
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| Online Access: | https://hdl.handle.net/1721.1/134178 |
| _version_ | 1826216407848714240 |
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| author | Gaglione, Domenico Soldi, Giovanni Meyer, Florian Hlawatsch, Franz Braca, Paolo Farina, Alfonso Win, Moe Z |
| author2 | Massachusetts Institute of Technology. Laboratory for Information and Decision Systems |
| author_facet | Massachusetts Institute of Technology. Laboratory for Information and Decision Systems Gaglione, Domenico Soldi, Giovanni Meyer, Florian Hlawatsch, Franz Braca, Paolo Farina, Alfonso Win, Moe Z |
| author_sort | Gaglione, Domenico |
| collection | MIT |
| description | © The Institution of Engineering and Technology 2020. The goal of maritime situational awareness (MSA) is to provide a seamless wide-area operational picture of ship traffic in coastal areas and the oceans in real time. Radar is a central sensing modality for MSA. In particular, oceanographic high-frequency surface-wave (HFSW) radars are attractive for surveying large sea areas at over-the-horizon distances, due to their low environmental footprint and low power requirements. However, their design is not optimal for the challenging conditions prevalent in MSA applications, thus calling for the development of dedicated information fusion and multisensor-multitarget tracking algorithms. In this study, the authors show how the multisensor-multitarget tracking problem can be formulated in a Bayesian framework and efficiently solved by running the loopy sum-product algorithm on a suitably devised factor graph. Compared to previously proposed methods, this approach is advantageous in terms of estimation accuracy, computational complexity, implementation flexibility, and scalability. Moreover, its performance can be further enhanced by estimating unknown model parameters in an online fashion and by fusing automatic identification system (AIS) data and context-based information. The effectiveness of the proposed Bayesian multisensor-multitarget tracking and information fusion algorithms is demonstrated through experimental results based on simulated data as well as real HFSW radar data and real AIS data. |
| first_indexed | 2024-09-23T16:46:51Z |
| format | Article |
| id | mit-1721.1/134178 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T16:46:51Z |
| publishDate | 2021 |
| publisher | Institution of Engineering and Technology (IET) |
| record_format | dspace |
| spelling | mit-1721.1/1341782023-01-20T21:48:15Z Bayesian information fusion and multitarget tracking for maritime situational awareness Gaglione, Domenico Soldi, Giovanni Meyer, Florian Hlawatsch, Franz Braca, Paolo Farina, Alfonso Win, Moe Z Massachusetts Institute of Technology. Laboratory for Information and Decision Systems © The Institution of Engineering and Technology 2020. The goal of maritime situational awareness (MSA) is to provide a seamless wide-area operational picture of ship traffic in coastal areas and the oceans in real time. Radar is a central sensing modality for MSA. In particular, oceanographic high-frequency surface-wave (HFSW) radars are attractive for surveying large sea areas at over-the-horizon distances, due to their low environmental footprint and low power requirements. However, their design is not optimal for the challenging conditions prevalent in MSA applications, thus calling for the development of dedicated information fusion and multisensor-multitarget tracking algorithms. In this study, the authors show how the multisensor-multitarget tracking problem can be formulated in a Bayesian framework and efficiently solved by running the loopy sum-product algorithm on a suitably devised factor graph. Compared to previously proposed methods, this approach is advantageous in terms of estimation accuracy, computational complexity, implementation flexibility, and scalability. Moreover, its performance can be further enhanced by estimating unknown model parameters in an online fashion and by fusing automatic identification system (AIS) data and context-based information. The effectiveness of the proposed Bayesian multisensor-multitarget tracking and information fusion algorithms is demonstrated through experimental results based on simulated data as well as real HFSW radar data and real AIS data. 2021-10-27T19:58:31Z 2021-10-27T19:58:31Z 2020 2021-05-05T16:57:28Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/134178 en 10.1049/IET-RSN.2019.0508 IET Radar Sonar and Navigation Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institution of Engineering and Technology (IET) Other repository |
| spellingShingle | Gaglione, Domenico Soldi, Giovanni Meyer, Florian Hlawatsch, Franz Braca, Paolo Farina, Alfonso Win, Moe Z Bayesian information fusion and multitarget tracking for maritime situational awareness |
| title | Bayesian information fusion and multitarget tracking for maritime situational awareness |
| title_full | Bayesian information fusion and multitarget tracking for maritime situational awareness |
| title_fullStr | Bayesian information fusion and multitarget tracking for maritime situational awareness |
| title_full_unstemmed | Bayesian information fusion and multitarget tracking for maritime situational awareness |
| title_short | Bayesian information fusion and multitarget tracking for maritime situational awareness |
| title_sort | bayesian information fusion and multitarget tracking for maritime situational awareness |
| url | https://hdl.handle.net/1721.1/134178 |
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