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小型无人机以其成本低、探测难、体积小等特性，在执行组网雷达干扰任务方面具有良好的应用前景。以高性能目标机突防组网雷达系统作为研究背景，提出一种基于联盟博弈和Shapley值（Shapley Value）估计算法的多无人机协同干扰决策方法，实现对突防无人机的有效掩护。基于组网中各雷达节点接收到的回波及干扰信号功率，设计包含干扰信号比率（Jamming-to-Signal Ratio,JSR）和子群规模在内的性能指标，建立协同干扰决策优化模型。以各干扰无人机和雷达节点作为参与者，以基于性能指标设计的参与者边际贡献度量（即Shapley值）作为效用函数，建立联盟博弈模型。考虑高性能目标机飞行过程的不确定性以及各干扰无人机位置变动带来的影响，提出基于Shapley值估计的协同动态决策算法，以满足环境变化对求解效率的要求。仿真结果表明，该优化方法能够在动态环境中有效降低组网雷达的干扰信号比率，提高协同干扰性能。
Abstract：Miniature unmanned aerial vehicles(UAVs) have good application prospects in performing netted radar interference tasks due to their characteristics of low cost, small size and low detectability.Considering the scenario of high-performance target UAV's penetration of the netted radar system, a multiUAVs collaborative jamming decision-making method is proposed based on the coalitional game and Shapley Value estimation algorithm to achieve an effective cover for UAV penetration. Based on the echo and jamming signal power received by each radar in the network, performance indices including jamming-to-signal ratio(JSR) and subgroup scale are designed to establish an optimization model for collaborative jamming decisionmaking. All jammer and radar are considered as players, and marginal contribution of the player(i.e., Shapley Value) which designed based on the performance indices is used as metrics to build a coalitional game model.Considering the uncertainty of the penetration UAV in flight and the impact of the jammers' position change, a collaborative dynamic decision-making algorithm based on the estimated Shapley Value is proposed to satisfy the demand of the environmental changes on the solving efficiency. Experimental results demonstrate that the the JSR of netted radars can be effectively suppressed in a dynamic environment by the proposed optimization method, improving the cooperative jamming performance.
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Basic Information:
DOI：10.16358/j.issn.1009-1300.20240099
China Classification Code:TN974;V279
Citation Information:
[1]刘溥熙,赵欣怡,尤明,等.面向组网雷达干扰任务的多无人机协同动态决策方法研究[J].战术导弹技术,2024,No.228(06):14-25.DOI:10.16358/j.issn.1009-1300.20240099.
Fund Information:
国家自然科学基金（62103417）