A Strong Maneuvering Target-Tracking Filtering Based on Intelligent Algorithm
In this paper, a variable-structure multimodel (VSMM) filtering algorithm based on the long short-term memory (LSTM) regression-deep Q network (L-DQN) is proposed to accurately track strong maneuvering targets. The algorithm can map the selection of the model set to the selection of the action label...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2024-01-01
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| Series: | International Journal of Aerospace Engineering |
| Online Access: | http://dx.doi.org/10.1155/2024/9981332 |
| _version_ | 1797351703555604480 |
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| author | Jing Li Xinru Liang Shengzhi Yuan Haiyan Li Changsheng Gao |
| author_facet | Jing Li Xinru Liang Shengzhi Yuan Haiyan Li Changsheng Gao |
| author_sort | Jing Li |
| collection | DOAJ |
| description | In this paper, a variable-structure multimodel (VSMM) filtering algorithm based on the long short-term memory (LSTM) regression-deep Q network (L-DQN) is proposed to accurately track strong maneuvering targets. The algorithm can map the selection of the model set to the selection of the action label and realize the purpose of a deep reinforcement-learning agent to replace the model switching in the traditional VSMM algorithm by reasonably designing a reward function, state space, and network structure. At the same time, the algorithm introduces a LSTM algorithm, which can compensate the error of tracking results based on model history information. The simulation results show that compared with the traditional VSMM algorithm, the proposed algorithm can quickly capture the maneuvering of the target, the response time is short, the calculation accuracy is significantly improved, and the range of adaptation is wider. Precise tracking of maneuvering targets was achieved. |
| first_indexed | 2024-03-08T13:04:22Z |
| format | Article |
| id | doaj.art-88fca4c97d4a485c97fc187a850e62b3 |
| institution | Directory Open Access Journal |
| issn | 1687-5974 |
| language | English |
| last_indexed | 2024-03-08T13:04:22Z |
| publishDate | 2024-01-01 |
| publisher | Hindawi Limited |
| record_format | Article |
| series | International Journal of Aerospace Engineering |
| spelling | doaj.art-88fca4c97d4a485c97fc187a850e62b32024-01-19T00:00:04ZengHindawi LimitedInternational Journal of Aerospace Engineering1687-59742024-01-01202410.1155/2024/9981332A Strong Maneuvering Target-Tracking Filtering Based on Intelligent AlgorithmJing Li0Xinru Liang1Shengzhi Yuan2Haiyan Li3Changsheng Gao4Naval University of EngineeringHarbin Institute of TechnologyNaval University of EngineeringNaval University of EngineeringHarbin Institute of TechnologyIn this paper, a variable-structure multimodel (VSMM) filtering algorithm based on the long short-term memory (LSTM) regression-deep Q network (L-DQN) is proposed to accurately track strong maneuvering targets. The algorithm can map the selection of the model set to the selection of the action label and realize the purpose of a deep reinforcement-learning agent to replace the model switching in the traditional VSMM algorithm by reasonably designing a reward function, state space, and network structure. At the same time, the algorithm introduces a LSTM algorithm, which can compensate the error of tracking results based on model history information. The simulation results show that compared with the traditional VSMM algorithm, the proposed algorithm can quickly capture the maneuvering of the target, the response time is short, the calculation accuracy is significantly improved, and the range of adaptation is wider. Precise tracking of maneuvering targets was achieved.http://dx.doi.org/10.1155/2024/9981332 |
| spellingShingle | Jing Li Xinru Liang Shengzhi Yuan Haiyan Li Changsheng Gao A Strong Maneuvering Target-Tracking Filtering Based on Intelligent Algorithm International Journal of Aerospace Engineering |
| title | A Strong Maneuvering Target-Tracking Filtering Based on Intelligent Algorithm |
| title_full | A Strong Maneuvering Target-Tracking Filtering Based on Intelligent Algorithm |
| title_fullStr | A Strong Maneuvering Target-Tracking Filtering Based on Intelligent Algorithm |
| title_full_unstemmed | A Strong Maneuvering Target-Tracking Filtering Based on Intelligent Algorithm |
| title_short | A Strong Maneuvering Target-Tracking Filtering Based on Intelligent Algorithm |
| title_sort | strong maneuvering target tracking filtering based on intelligent algorithm |
| url | http://dx.doi.org/10.1155/2024/9981332 |
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