Research on Unmanned Underwater Vehicle Threat Assessment
The unmanned underwater vehicle (UUV) plays an ever increasing and important role in the modern marine environment. In particular, the tasks of underwater reconnaissance and surveillance, underwater mine hunting and anti-submarine warfare, all poses a serious and dangerous threat to humans. UUV has...
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| Online Access: | https://ieeexplore.ieee.org/document/8611187/ |
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doaj-79b17d0101564768be1d91c201e291a82021-03-29T22:02:05ZengIEEEIEEE Access2169-35362019-01-017113871139610.1109/ACCESS.2019.28919408611187Research on Unmanned Underwater Vehicle Threat AssessmentHongfei Yao0https://orcid.org/0000-0002-4608-6116Hongjian Wang1Yiming Li2Ying Wang3Chunsong Han4College of Automation, Harbin Engineering University, Harbin, ChinaCollege of Automation, Harbin Engineering University, Harbin, ChinaCollege of Automation, Harbin Engineering University, Harbin, ChinaCollege of Automation, Harbin Engineering University, Harbin, ChinaCollege of Mechanical Engineering, Qiqihar University, Qiqihar, ChinaThe unmanned underwater vehicle (UUV) plays an ever increasing and important role in the modern marine environment. In particular, the tasks of underwater reconnaissance and surveillance, underwater mine hunting and anti-submarine warfare, all poses a serious and dangerous threat to humans. UUV has become the fore-running technology to accomplish such missions. In this paper, a method based on dynamic Bayesian network modeling was proposed to evaluate the UUV in an underwater threat situation. We divided the UUV threats into three categories: environmental factors, platform factors, and mission factors. Through each of these categories, we carried out factor extraction and set up the priori probability according to the characteristics. Setting up the static Bayesian network involved the addition of state transition probability and establishment of the model for assessing the dynamic Bayesian threat situation. By comparing the results of the static and dynamic Bayesian simulation, it was shown that the dynamic Bayesian is superior. Moreover, by analyzing the sensitivity, we recognized the greatest current threat and in response, determined the appropriate UUV countermeasures. The results showed that the dynamic Bayesian method has great practical significance and value for threat assessment.https://ieeexplore.ieee.org/document/8611187/Dynamic Bayesianthreat assessmentunmanned underwater vehicle |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Hongfei Yao Hongjian Wang Yiming Li Ying Wang Chunsong Han |
| spellingShingle |
Hongfei Yao Hongjian Wang Yiming Li Ying Wang Chunsong Han Research on Unmanned Underwater Vehicle Threat Assessment IEEE Access Dynamic Bayesian threat assessment unmanned underwater vehicle |
| author_facet |
Hongfei Yao Hongjian Wang Yiming Li Ying Wang Chunsong Han |
| author_sort |
Hongfei Yao |
| title |
Research on Unmanned Underwater Vehicle Threat Assessment |
| title_short |
Research on Unmanned Underwater Vehicle Threat Assessment |
| title_full |
Research on Unmanned Underwater Vehicle Threat Assessment |
| title_fullStr |
Research on Unmanned Underwater Vehicle Threat Assessment |
| title_full_unstemmed |
Research on Unmanned Underwater Vehicle Threat Assessment |
| title_sort |
research on unmanned underwater vehicle threat assessment |
| publisher |
IEEE |
| series |
IEEE Access |
| issn |
2169-3536 |
| publishDate |
2019-01-01 |
| description |
The unmanned underwater vehicle (UUV) plays an ever increasing and important role in the modern marine environment. In particular, the tasks of underwater reconnaissance and surveillance, underwater mine hunting and anti-submarine warfare, all poses a serious and dangerous threat to humans. UUV has become the fore-running technology to accomplish such missions. In this paper, a method based on dynamic Bayesian network modeling was proposed to evaluate the UUV in an underwater threat situation. We divided the UUV threats into three categories: environmental factors, platform factors, and mission factors. Through each of these categories, we carried out factor extraction and set up the priori probability according to the characteristics. Setting up the static Bayesian network involved the addition of state transition probability and establishment of the model for assessing the dynamic Bayesian threat situation. By comparing the results of the static and dynamic Bayesian simulation, it was shown that the dynamic Bayesian is superior. Moreover, by analyzing the sensitivity, we recognized the greatest current threat and in response, determined the appropriate UUV countermeasures. The results showed that the dynamic Bayesian method has great practical significance and value for threat assessment. |
| topic |
Dynamic Bayesian threat assessment unmanned underwater vehicle |
| url |
https://ieeexplore.ieee.org/document/8611187/ |
| work_keys_str_mv |
AT hongfeiyao researchonunmannedunderwatervehiclethreatassessment AT hongjianwang researchonunmannedunderwatervehiclethreatassessment AT yimingli researchonunmannedunderwatervehiclethreatassessment AT yingwang researchonunmannedunderwatervehiclethreatassessment AT chunsonghan researchonunmannedunderwatervehiclethreatassessment |
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