电子学报 ›› 2015, Vol. 43 ›› Issue (9): 1673-1681.DOI: 10.3969/j.issn.0372-2112.2015.09.001

• 学术论文 •    下一篇

基于PPSO-MPC的多雷达协同反隐身指示搜索任务规划

高晓光, 万开方, 李波, 李飞   

  1. 西北工业大学电子信息学院, 陕西西安 710129
  • 收稿日期:2014-03-10 修回日期:2014-05-28 出版日期:2015-09-25
    • 作者简介:
    • 高晓光 女,1957年出生于辽宁鞍山,博士学位,现为西北工业大学电子信息学院教授、博士生导师,主要研究方向:先进火力控制原理、复杂系统建模理论与效能分析、传感器协同管理与电子对抗等.E-mail:cxg2012@nwpu.edu.cn;万开方 男,1987年出生于湖北随州,现为西北工业大学电子信息学院博士研究生,主要研究方向为:传感器协同管理与电子对抗,先进火力控制原理、复杂系统建模理论与效能分析等.E-mail:yibai_2003@126.com;李波 男,1978年出生于山东泰安,博士学位,现为西北工业大学电子信息学院副教授,主要研究方向为:决策理论、先进航空火力控制.E-mail:libo803@nwpu.edu.cn;李飞 男,1988年出生于山东潍坊,现为西北工业大学电子信息学院博士研究生,主要研究方向为:传感器协同管理与电子对抗,先进火力控制原理、复杂系统建模理论与效能分析等.E-mail:nwpulf@nwpu.mail.edu.cn
    • 基金资助:
    • 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)

Mission Planning for Cued Search of Cooperative Anti-Stealth Detection Based on PPSO-MPC

GAO Xiao-guang, WAN Kai-fang, LI Bo, LI Fei   

  1. School of Electronics and Information, Northwestern Polytechnical University, Xi'an, Shaanxi 710129, China
  • Received:2014-03-10 Revised:2014-05-28 Online:2015-09-25 Published:2015-09-25
    • Supported by:
    • National Natural Science Foundation of China (No.61305133); Doctoral Foundation of Colleges and Universities of China (No.20116102110026); Space Technology Support Fund (No.2013-HT-XGD); Funded by Fundamental Research Funds for the Central Universities (No.3102015ZY092)

摘要:

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.

关键词: 反隐身, 指示搜索, MPC, 任务规划, 滚动优化, PSO

Abstract:

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.

Key words: anti-stealth, cued search, MPC, mission planning, receding optimization, PSO

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