The unmanned and intelligent ship-to-air defense system has emerged as a prominent development trend. Deep reinforcement learning is deemed applicable to combat command decision-making, offering potential to enhance combat effectiveness and reduce risk. However, there is a paucity of research on constructing intelligent models for ship-to-air defense problem in ship formation utilizing centroid jamming. To address this gap, we developed the two-dimensional model for centroid jamming scenario, and proposed a decision-making model based on the Markov decision-making process. This model aims to unify high-dimensional decision-making, encompassing the chaff cloud deployment and multi-ship maneuvering. Additionally, a threat level assessment model for enemy anti-ship missile is established to enhance the efficiency and success rate of the decision-making algorithm. Finally, the paper presents tests conducted on ship fleet of varying sizes and formations in diverse wind force environments, followed by an analysis of the results.


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    Titel :

    Decision-Making for Ship Formation Centroid Jamming Based on Reinforcement Learning


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Yan, Liang (Herausgeber:in) / Duan, Haibin (Herausgeber:in) / Deng, Yimin (Herausgeber:in) / Chen, Yiran (Autor:in) / Yi, Guoxing (Autor:in) / Wang, Hao (Autor:in) / Zhang, Yisong (Autor:in) / Cheng, Yu (Autor:in) / Wei, Zhennan (Autor:in)

    Kongress:

    International Conference on Guidance, Navigation and Control ; 2024 ; Changsha, China August 09, 2024 - August 11, 2024



    Erscheinungsdatum :

    11.03.2025


    Format / Umfang :

    11 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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