Path planning method for maritime dynamic target search based on improved GBNN
Abstract To address the issues of low discovery probability, inefficient search, and antagonistic targets during the process of dynamic target search in the ocean, a dynamic target search path planning method based on the Glasius biologically-inspired neural network (GBNN) in combination with marine...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2025-05-01
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| Series: | Complex & Intelligent Systems |
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| Online Access: | https://doi.org/10.1007/s40747-025-01914-9 |
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| _version_ | 1849685875641810944 |
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| author | Zhaozhen Jiang Xuehai Sun Wenlon Wang Shuzeng Zhou Qiang Li Lianglong Da |
| author_facet | Zhaozhen Jiang Xuehai Sun Wenlon Wang Shuzeng Zhou Qiang Li Lianglong Da |
| author_sort | Zhaozhen Jiang |
| collection | DOAJ |
| description | Abstract To address the issues of low discovery probability, inefficient search, and antagonistic targets during the process of dynamic target search in the ocean, a dynamic target search path planning method based on the Glasius biologically-inspired neural network (GBNN) in combination with marine environmental information is proposed. Firstly, the motion model of the searcher and the capability model of sonar detection are established, and the dynamic motion characteristics of the target are analyzed. The Beta distribution is employed to characterize the variation of the target velocity, and the distribution probability map of the target position alterations over time is obtained. Then GBNN is presented and the marine environment information is integrated to enhance the calculation approach of the internal connection weights of the network. Moreover, the update rule of the activity value of the neural network is reconfigured. The influence of the peak of the dynamic target distribution probability on the activity value of the neuron is regarded as the external incentive element. According to the turning limitation of the searcher and the activity of GBNN neurons, the search path points are determined smoothly. The paper's algorithm, validated through 10,000 Monte Carlo simulations with real maritime data, significantly outperforms traditional search methods in the discovery probability and search efficiency. |
| format | Article |
| id | doaj-art-25d8c0c8d9d14992ad27a29eec4e89ae |
| institution | DOAJ |
| issn | 2199-4536 2198-6053 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj-art-25d8c0c8d9d14992ad27a29eec4e89ae2025-08-20T03:22:57ZengSpringerComplex & Intelligent Systems2199-45362198-60532025-05-0111711810.1007/s40747-025-01914-9Path planning method for maritime dynamic target search based on improved GBNNZhaozhen Jiang0Xuehai Sun1Wenlon Wang2Shuzeng Zhou3Qiang Li4Lianglong Da5Naval Submarine AcademyNaval Submarine AcademyNaval Submarine AcademyNaval Submarine AcademyNaval Submarine AcademyNaval Submarine AcademyAbstract To address the issues of low discovery probability, inefficient search, and antagonistic targets during the process of dynamic target search in the ocean, a dynamic target search path planning method based on the Glasius biologically-inspired neural network (GBNN) in combination with marine environmental information is proposed. Firstly, the motion model of the searcher and the capability model of sonar detection are established, and the dynamic motion characteristics of the target are analyzed. The Beta distribution is employed to characterize the variation of the target velocity, and the distribution probability map of the target position alterations over time is obtained. Then GBNN is presented and the marine environment information is integrated to enhance the calculation approach of the internal connection weights of the network. Moreover, the update rule of the activity value of the neural network is reconfigured. The influence of the peak of the dynamic target distribution probability on the activity value of the neuron is regarded as the external incentive element. According to the turning limitation of the searcher and the activity of GBNN neurons, the search path points are determined smoothly. The paper's algorithm, validated through 10,000 Monte Carlo simulations with real maritime data, significantly outperforms traditional search methods in the discovery probability and search efficiency.https://doi.org/10.1007/s40747-025-01914-9Path planningDynamic target searchBeta distributionImproved GBNNPeak excitation |
| spellingShingle | Zhaozhen Jiang Xuehai Sun Wenlon Wang Shuzeng Zhou Qiang Li Lianglong Da Path planning method for maritime dynamic target search based on improved GBNN Complex & Intelligent Systems Path planning Dynamic target search Beta distribution Improved GBNN Peak excitation |
| title | Path planning method for maritime dynamic target search based on improved GBNN |
| title_full | Path planning method for maritime dynamic target search based on improved GBNN |
| title_fullStr | Path planning method for maritime dynamic target search based on improved GBNN |
| title_full_unstemmed | Path planning method for maritime dynamic target search based on improved GBNN |
| title_short | Path planning method for maritime dynamic target search based on improved GBNN |
| title_sort | path planning method for maritime dynamic target search based on improved gbnn |
| topic | Path planning Dynamic target search Beta distribution Improved GBNN Peak excitation |
| url | https://doi.org/10.1007/s40747-025-01914-9 |
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