CAJ | 학술논문

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。
침대 ESM／뢰체협동반은신탐측중적지시수색문제，인입모형예측공제（Model Predictive Control，MPC）이론，급출지시수색임무규화적 MPC 광가，건립지시수색적목표상태예측모형화재선곤동우화모형。침대모형구해，인입입자군우화（Particle Swarm Optimization，PSO）산법，설계료고유구진입자편마방식，인입척도계산인자처리변계약속，인입개솔모형처리리산변량，설계실현료일충“다주절점-단종절점”적（Multi-Master-Single-Slave，MM-SS）다충군병행계산책략。방진결과표명，소건립적모형능구재불학정、다목표배경하실현대다뢰체적고효협동공제，소제출적모형구해산법능구실현대곤동우화문제적쾌속、고효구해，즉모형화산법적유효성득도료험증。
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 paral-lel 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 effi-ciency.Experiments show that the established model realizes an efficient control of multi-radars in condition of uncertainty and mul-tiple targets,and that the proposed algorithm can solve the receding optimization problem efficiently.That is,the validity of the mod-el and algorithm is demonstrated.