Assessment of underwater navigation safety based on dynamic Bayesian network facing uncertain knowledge and various information
As ocean environment is complicated and varied, underwater vehicles (UVs) are facing great challenges in safe and precise navigation. Therefore, it is important to evaluate the underwater ocean environment safety for the UV navigation. To deal with the uncertain knowledge and various information in...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-12-01
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| Series: | Frontiers in Marine Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fmars.2022.1069841/full |
| _version_ | 1811206270155227136 |
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| author | Ming Li Ren Zhang Xi Chen Kefeng Liu |
| author_facet | Ming Li Ren Zhang Xi Chen Kefeng Liu |
| author_sort | Ming Li |
| collection | DOAJ |
| description | As ocean environment is complicated and varied, underwater vehicles (UVs) are facing great challenges in safe and precise navigation. Therefore, it is important to evaluate the underwater ocean environment safety for the UV navigation. To deal with the uncertain knowledge and various information in the safety assessment, we present an evaluation model based on the dynamic Bayesian network (DBN) theory. Firstly, characteristic indicators are extract from marine environment systems and discretized with Cloud model. Then, the DBN is constructed through structure learning and parameter learning based on Dempster-Shafer (DS) evidence theory. Finally, the dynamic evaluation and risk zoning of the navigation safety is realized based on Bayesian probabilistic reasoning. The DBN-based assessment model fully considers the uncertainty of influence relationships between marine environment and UV navigation, and effectively fuses expert knowledge and quantitative data for assessment modeling. The experimental results show the proposed model has high reliability and good value of application. |
| first_indexed | 2024-04-12T03:44:52Z |
| format | Article |
| id | doaj.art-09d8fb284a154e25ba308c9c24857dc1 |
| institution | Directory Open Access Journal |
| issn | 2296-7745 |
| language | English |
| last_indexed | 2024-04-12T03:44:52Z |
| publishDate | 2022-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Marine Science |
| spelling | doaj.art-09d8fb284a154e25ba308c9c24857dc12022-12-22T03:49:10ZengFrontiers Media S.A.Frontiers in Marine Science2296-77452022-12-01910.3389/fmars.2022.10698411069841Assessment of underwater navigation safety based on dynamic Bayesian network facing uncertain knowledge and various informationMing Li0Ren Zhang1Xi Chen2Kefeng Liu3College of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha, ChinaCollaborative Innovation Center on Meteorological Disaster Forecast, Warning and Assessment, Nanjing University of Information Science and Engineering, Nanjing, ChinaCollege of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha, ChinaCollege of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha, ChinaAs ocean environment is complicated and varied, underwater vehicles (UVs) are facing great challenges in safe and precise navigation. Therefore, it is important to evaluate the underwater ocean environment safety for the UV navigation. To deal with the uncertain knowledge and various information in the safety assessment, we present an evaluation model based on the dynamic Bayesian network (DBN) theory. Firstly, characteristic indicators are extract from marine environment systems and discretized with Cloud model. Then, the DBN is constructed through structure learning and parameter learning based on Dempster-Shafer (DS) evidence theory. Finally, the dynamic evaluation and risk zoning of the navigation safety is realized based on Bayesian probabilistic reasoning. The DBN-based assessment model fully considers the uncertainty of influence relationships between marine environment and UV navigation, and effectively fuses expert knowledge and quantitative data for assessment modeling. The experimental results show the proposed model has high reliability and good value of application.https://www.frontiersin.org/articles/10.3389/fmars.2022.1069841/fullunderwater navigationsafety assessmentBayesian networkuncertaintyincomplete information |
| spellingShingle | Ming Li Ren Zhang Xi Chen Kefeng Liu Assessment of underwater navigation safety based on dynamic Bayesian network facing uncertain knowledge and various information Frontiers in Marine Science underwater navigation safety assessment Bayesian network uncertainty incomplete information |
| title | Assessment of underwater navigation safety based on dynamic Bayesian network facing uncertain knowledge and various information |
| title_full | Assessment of underwater navigation safety based on dynamic Bayesian network facing uncertain knowledge and various information |
| title_fullStr | Assessment of underwater navigation safety based on dynamic Bayesian network facing uncertain knowledge and various information |
| title_full_unstemmed | Assessment of underwater navigation safety based on dynamic Bayesian network facing uncertain knowledge and various information |
| title_short | Assessment of underwater navigation safety based on dynamic Bayesian network facing uncertain knowledge and various information |
| title_sort | assessment of underwater navigation safety based on dynamic bayesian network facing uncertain knowledge and various information |
| topic | underwater navigation safety assessment Bayesian network uncertainty incomplete information |
| url | https://www.frontiersin.org/articles/10.3389/fmars.2022.1069841/full |
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