Bayesian information fusion and multitarget tracking for maritime situational awareness
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...
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| Vydané v: | IET radar, sonar & navigation Ročník 14; číslo 12; s. 1845 - 1857 |
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| Hlavní autori: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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The Institution of Engineering and Technology
01.12.2020
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| ISSN: | 1751-8784, 1751-8792 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Gaglione, Domenico Soldi, Giovanni Hlawatsch, Franz Win, Moe Z Meyer, Florian Farina, Alfonso Braca, Paolo |
| Author_xml | – sequence: 1 givenname: Domenico orcidid: 0000-0001-7401-1659 surname: Gaglione fullname: Gaglione, Domenico email: domenico.gaglione@cmre.nato.int organization: 1NATO Centre for Maritime Research and Experimentation (CMRE), La Spezia, Italy – sequence: 2 givenname: Giovanni surname: Soldi fullname: Soldi, Giovanni organization: 1NATO Centre for Maritime Research and Experimentation (CMRE), La Spezia, Italy – sequence: 3 givenname: Florian surname: Meyer fullname: Meyer, Florian organization: 2Scripps Institution of Oceanography and Electrical and Computer Engineering Department, University of California San Diego, La Jolla, CA, USA – sequence: 4 givenname: Franz surname: Hlawatsch fullname: Hlawatsch, Franz organization: 3Institute of Telecommunications, TU Wien, Vienna, Austria – sequence: 5 givenname: Paolo surname: Braca fullname: Braca, Paolo organization: 1NATO Centre for Maritime Research and Experimentation (CMRE), La Spezia, Italy – sequence: 6 givenname: Alfonso surname: Farina fullname: Farina, Alfonso organization: 4Professional Consultant, Rome, Italy – sequence: 7 givenname: Moe Z surname: Win fullname: Win, Moe Z organization: 5Laboratory for Information and Decision Systems (LIDS), Massachusetts Institute of Technology (MIT), Cambridge, MA, USA |
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| CitedBy_id | crossref_primary_10_1016_j_dt_2024_08_017 crossref_primary_10_1016_j_robot_2023_104508 crossref_primary_10_1038_s41598_021_97461_7 crossref_primary_10_1016_j_ast_2023_108605 crossref_primary_10_1109_ACCESS_2021_3052506 crossref_primary_10_1109_TCCN_2023_3307953 crossref_primary_10_1109_JSEN_2023_3323323 crossref_primary_10_3390_s25175510 crossref_primary_10_1109_MAES_2023_3285075 crossref_primary_10_1109_MAES_2024_3366139 crossref_primary_10_3390_s24113458 crossref_primary_10_1016_j_oceaneng_2024_119368 crossref_primary_10_1016_j_oceaneng_2021_109303 crossref_primary_10_3390_jmse12122315 crossref_primary_10_1109_MAES_2021_3070884 crossref_primary_10_1016_j_oceaneng_2022_110555 crossref_primary_10_1109_TITS_2021_3123890 crossref_primary_10_3390_s25061676 |
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| Keywords | oceanographic techniques radar detection dedicated information fusion loopy sum-product algorithm information fusion algorithms sensor fusion context-based information sea areas tracking algorithms Bayesian information fusion coastal areas ship traffic multisensor-multitarget tracking problem high-frequency surface-wave radars MSA applications HFSW radar data marine radar radar tracking low power requirements Bayesian framework geophysical signal processing low environmental footprint maritime situational awareness wide-area operational picture ships target tracking central sensing modality Bayes methods suitably devised factor graph over-the-horizon distances authors |
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| Snippet | 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... 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... |
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| SubjectTerms | authors Bayes methods Bayesian framework Bayesian information fusion central sensing modality coastal areas context‐based information dedicated information fusion geophysical signal processing HFSW radar data high‐frequency surface‐wave radars information fusion algorithms loopy sum‐product algorithm low environmental footprint low power requirements marine radar maritime situational awareness MSA applications multisensor‐multitarget tracking problem oceanographic techniques over‐the‐horizon distances radar detection radar tracking Research Article sea areas sensor fusion ship traffic ships suitably devised factor graph target tracking tracking algorithms wide‐area operational picture |
| Title | Bayesian information fusion and multitarget tracking for maritime situational awareness |
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