Method of on-call submarine searching for surface ship formation based on hidden Markov model

<b>[Objectives]</b> In order to improve the successful probability of searching for target submarines and to make the operation of the surface ship formation more effectively,the problem about the path planning for the on-call submarine searching for ships is studied.<b>[Methods]&l...

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Main Authors: Bian Dapeng, Yu Shanshan, Zhang Shi, Yu Minghui, Wang Yun
Format: Article
Language:English
Published: Editorial Office of Chinese Journal of Ship Research 2019-12-01
Series:Zhongguo Jianchuan Yanjiu
Subjects:
Online Access:http://www.ship-research.com/EN/Y2019/V14/I6/192
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spelling doaj-b725aa64de3f4df4a8acc8aba9c442352020-11-25T02:20:06ZengEditorial Office of Chinese Journal of Ship ResearchZhongguo Jianchuan Yanjiu1673-31851673-31852019-12-0114619220010.19693/j.issn.1673-3185.01507201906024Method of on-call submarine searching for surface ship formation based on hidden Markov modelBian Dapeng0Yu Shanshan1Zhang Shi2Yu Minghui3Wang Yun4Wuhan Second Military Representative Office, Naval Armament Department of PLAN, Wuhan 430064, ChinaSchool of Automation, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Automation, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Automation, Huazhong University of Science and Technology, Wuhan 430074, ChinaChina Ship Development and Design Center, Wuhan 430064, China<b>[Objectives]</b> In order to improve the successful probability of searching for target submarines and to make the operation of the surface ship formation more effectively,the problem about the path planning for the on-call submarine searching for ships is studied.<b>[Methods]</b> Firstly, an on-call submarine searching model of surface ships was constructed based on the Hidden Markov Model(HMM). A two-stage heuristic method was designed to maximize the probability of searching for submarine search expectation. The problem of local optimum was avoided by using evolutionary algorithm(EA)to cross and mutate the individuals in the population,and a comparison with conventional searching methods was made. Then, the effects of different segmentation methods on path optimization were studied experimentally.<b>[Results]</b> The simulation results of single-ship and multi-ship searching for submarines show that the method adopted in this paper can obtain the maximum submarine search expectation and the optimal searching path. The segmentation times experiment show that a reasonable re-division of the search area is beneficial to find a better searching path.<b>[Conclusions]</b> This model can find an optimal path for submarine search and improve the searching efficiency of the surface ship formation.http://www.ship-research.com/EN/Y2019/V14/I6/192hidden markov model(hmm)on-call submarine searchingevolutionary algorithm(ea)path optimization
collection DOAJ
language English
format Article
sources DOAJ
author Bian Dapeng
Yu Shanshan
Zhang Shi
Yu Minghui
Wang Yun
spellingShingle Bian Dapeng
Yu Shanshan
Zhang Shi
Yu Minghui
Wang Yun
Method of on-call submarine searching for surface ship formation based on hidden Markov model
Zhongguo Jianchuan Yanjiu
hidden markov model(hmm)
on-call submarine searching
evolutionary algorithm(ea)
path optimization
author_facet Bian Dapeng
Yu Shanshan
Zhang Shi
Yu Minghui
Wang Yun
author_sort Bian Dapeng
title Method of on-call submarine searching for surface ship formation based on hidden Markov model
title_short Method of on-call submarine searching for surface ship formation based on hidden Markov model
title_full Method of on-call submarine searching for surface ship formation based on hidden Markov model
title_fullStr Method of on-call submarine searching for surface ship formation based on hidden Markov model
title_full_unstemmed Method of on-call submarine searching for surface ship formation based on hidden Markov model
title_sort method of on-call submarine searching for surface ship formation based on hidden markov model
publisher Editorial Office of Chinese Journal of Ship Research
series Zhongguo Jianchuan Yanjiu
issn 1673-3185
1673-3185
publishDate 2019-12-01
description <b>[Objectives]</b> In order to improve the successful probability of searching for target submarines and to make the operation of the surface ship formation more effectively,the problem about the path planning for the on-call submarine searching for ships is studied.<b>[Methods]</b> Firstly, an on-call submarine searching model of surface ships was constructed based on the Hidden Markov Model(HMM). A two-stage heuristic method was designed to maximize the probability of searching for submarine search expectation. The problem of local optimum was avoided by using evolutionary algorithm(EA)to cross and mutate the individuals in the population,and a comparison with conventional searching methods was made. Then, the effects of different segmentation methods on path optimization were studied experimentally.<b>[Results]</b> The simulation results of single-ship and multi-ship searching for submarines show that the method adopted in this paper can obtain the maximum submarine search expectation and the optimal searching path. The segmentation times experiment show that a reasonable re-division of the search area is beneficial to find a better searching path.<b>[Conclusions]</b> This model can find an optimal path for submarine search and improve the searching efficiency of the surface ship formation.
topic hidden markov model(hmm)
on-call submarine searching
evolutionary algorithm(ea)
path optimization
url http://www.ship-research.com/EN/Y2019/V14/I6/192
work_keys_str_mv AT biandapeng methodofoncallsubmarinesearchingforsurfaceshipformationbasedonhiddenmarkovmodel
AT yushanshan methodofoncallsubmarinesearchingforsurfaceshipformationbasedonhiddenmarkovmodel
AT zhangshi methodofoncallsubmarinesearchingforsurfaceshipformationbasedonhiddenmarkovmodel
AT yuminghui methodofoncallsubmarinesearchingforsurfaceshipformationbasedonhiddenmarkovmodel
AT wangyun methodofoncallsubmarinesearchingforsurfaceshipformationbasedonhiddenmarkovmodel
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