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

  • 会议论文

  • 发表日期：

    2025年

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  • Yiran Chen,
  • Guoxing Yi,
  • Hao Wang,
  • Yisong Zhang,
  • Yu Cheng,
  • Zhennan Wei

摘要

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.