Ban Honghui, Pan Jifei, Wang Zheng, Cui Rui, Ming Yuting, Jiang Qiuxi
College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China.
Entropy (Basel). 2025 Jun 18;27(6):653. doi: 10.3390/e27060653.
In complex electromagnetic environments, spatial coupling uncertainties-position errors and timing jitter-increase false target information entropy, reducing strategy effectiveness and posing challenges for robust UAV swarm track deception. This paper proposes an error-constrained entropy-minimizing compensation framework to model radar/UAV errors and their spatial coupling. The framework establishes closed-form gate association conditions based on the principle of entropy minimization, ensuring mutual consistency of false target measurements across multiple radars. Two strategies are proposed to reduce false target information entropy: 1. Zonal track compensation forms dense "information entropy bands" around each preset false target by inserting auxiliary deception echoes, enhancing mutual information concentration in the measurement space; 2. Formation jamming compensation adaptively reshapes the UAV swarm into regular polygons, leveraging geometric symmetry to suppress spatial diffusion of position errors. Simulation results show that compared with traditional methods, the proposed approach reduces the spatial inconsistency entropy by 50%, improving false target consistency and radar deception reliability.
在复杂电磁环境中，空间耦合不确定性（位置误差和定时抖动）会增加虚假目标信息熵，降低策略有效性，并给稳健的无人机集群航迹欺骗带来挑战。本文提出了一种误差约束熵最小化补偿框架，用于对雷达/无人机误差及其空间耦合进行建模。该框架基于熵最小化原理建立了闭式门关联条件，确保多个雷达间虚假目标测量的相互一致性。提出了两种降低虚假目标信息熵的策略：1. 区域航迹补偿通过插入辅助欺骗回波在每个预设虚假目标周围形成密集的“信息熵带”，增强测量空间中的互信息集中度；2. 编队干扰补偿将无人机集群自适应地重塑为正多边形，利用几何对称性抑制位置误差的空间扩散。仿真结果表明，与传统方法相比，所提方法将空间不一致熵降低了50%，提高了虚假目标一致性和雷达欺骗可靠性。
