跳频周期和跳频间隔的最大条件熵射频隐身设计方法

摘要

In order to increase the interception performance of frequency hopping systems, an optimal algorithm for hopping cycle and hopping interval, which is based on the conditional maximum entropy is proposed. The prior data of frequency hopping systems are used as the training sample space, and the Lagrange multipliers are selected as optimized variables. The Hybrid Chaotic Particle Swarm Optimization (HCPSO) algorithm is used for the optimization of the dual programming of the conditional maximum entropy. Compared with the Single Threshold Method (STM) and the Double Threshold Method (DTM), the simulation results show that the proposed Maximum Entropy Method (MEM) not only has the greatest uncertainty of hopping cycle and hopping interval, it also has the lowest probability of intercept and higher environmental differentiation with threat factors. So the MEM has good RF stealth performance and it can effectively improve the survival ability of the platform.%为提高跳频通信系统的被截获性能，该文以跳频周期和跳频间隔为研究对象，提出一种基于最大条件熵的跳频周期、跳频间隔射频隐身联合设计方法。该方法以跳频系统先验数据为样本空间，以拉格朗日乘子为优化粒子，利用混合混沌粒子群优化(HCPSO)算法对最大条件熵的对偶规划进行优化计算。与单阈值方法(STM)和双阈值方法(DTM)的对比仿真结果表明：该文提出的最大熵方法(MEM)具有最大的跳频周期、跳频间隔不确定性和最小的截获概率，且针对不同威胁因子具有较高的环境区分度。因此，MEM具有较好的射频隐身性能，可以有效提高搭载平台的生存能力。