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基于PPSO-MPC的多雷达协同反隐身指示搜索任务规划
Mission Planning for Cued Search of Cooperative Anti-Stealth Detection Based on PPSO-MPC
针对ESM/雷达协同反隐身探测中的指示搜索问题,引入模型预测控制(Model Predictive Control,MPC)理论,给出指示搜索任务规划的MPC框架,建立指示搜索的目标状态预测模型和在线滚动优化模型.针对模型求解,引入粒子群优化(Particle Swarm Optimization,PSO)算法,设计了高维矩阵粒子编码方式,引入尺度计算因子处理边界约束,引入概率模型处理离散变量,设计实现了一种"多主节点-单从节点"的 (Multi-Master-Single-Slave,MM-SS)多种群并行计算策略.仿真结果表明,所建立的模型能够在不确定、多目标环境下实现对多雷达的高效协同控制,所提出的模型求解算法能够实现对滚动优化问题的快速、高效求解,即模型和算法的有效性得到了验证.
To solve the cued search problem when ESMs and radars cooperate with each other in anti-stealth detection,a MPC-based(Model Predictive Control) mission planning frame for cued search is proposed,and the targets' states predictive model and on-line receding optimization model are established based on the MPC theory.Then,this paper puts forward an improved parallel PSO(Particle Swarm Optimization) algorithm to solve the problem.Concretely,a high-dimensional matrix mode is designed for particle coding,a scale-factor is imported for boundary restriction,a probabilistic model is proposed for processing discrete variable,and a new multi-swarm parallel strategy called MM-SS(Multi-Master-Single-Slave) is presented for promoting optimization efficiency.Experiments show that the established model realizes an efficient control of multi-radars in condition of uncertainty and multiple targets,and that the proposed algorithm can solve the receding optimization problem efficiently.That is,the validity of the model and algorithm is demonstrated.
反隐身 / 指示搜索 / MPC / 任务规划 / 滚动优化 / PSO {{custom_keyword}} /
anti-stealth / cued search / MPC / mission planning / receding optimization / PSO {{custom_keyword}} /
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国家自然科学基金 (No.61305133); 全国高校博士点基金 (No.20116102110026); 航天技术支撑基金 (No.2013-HT-XGD); 中央高校基本科研业务专项资金资助 (No.3102015ZY092)
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