针对在随机优化环境下的距离门拖引干扰策略优化问题,提出一种基于假设检验的计算预算分配-粒子群优化的距离门拖引干扰建模与策略优化算法。首先,将距离门拖引干扰策略的优化问题建模为一个随机仿真优化问题,并提出一种不依赖于敌方雷达跟踪系统信息的拖引干扰策略评价方案;然后,为提升距离门拖引干扰策略性能,提出一种基于假设检验的计算预算分配-粒子群优化的距离门拖引干扰策略优化方法;最后,通过数值仿真验证了该方法的有效性。

A hypothesis test based computing budget allocation combined with particle swarm optimization algorithm is proposed for range gate pull-off(RGPO) jamming strategy optimization problem in a stochastic optimization environment. Firstly, range gate pull-off jamming strategy optimization is modeled as a stochastic simulation optimization problem, and we propose a range gate pull-off jamming strategy evaluation scheme that does not rely on the internal information of the radar tracking system. Then, to improve the performance of the range gate pull-off jamming strategy, we propose a hypothesis test based computing budget allocation combined with particle swarm optimization algorithm for range gate pull-off jamming strategy optimization. Finally, numerical results are provided to verify the validity of the proposed method.

TN972

国家自然科学基金资助面上项目(61971109);国防科技创新特区支持项目(重点项目);中央高校基本科研业务费资助项目(ZYGX2018J009)