摘要
本文在静态传感器–武器–目标分配(S–WTA)问题的基础上,将作战过程分为多个拦截阶段,以最小化来袭目标的剩余威胁的期望值为目标,建立了一种多阶段S–WTA问题模型.为了求解该问题,本文将多阶段S–WTA问题分解为两类作战资源分配子问题.首先,提出了一种基于知识的增量式构造型启发式算法对多阶段武器–目标分配子问题进行求解.根据已确定的多阶段武器–目标分配方案,提出了一种基于边际损失的构造型启发式算法求解多阶段传感器–目标分配子问题.结合两种低复杂度、快速构造型启发式算法实现多阶段S–WTA问题的有效求解.本文选取了基于随机排列(RP)的随机采样算法作为对比算法,并通过仿真实验验证了算法的有效性.实验结果表明,本文提出的算法在大部分算例的求解质量和时间成本上都优于RP算法.
Based on the static variant of the sensor-weapon-target assignment(S–WTA) problem, we built a mathematical model for the multi-stage S–WTA problem, with the objective of minimizing the expected remaining threat value of the incoming targets, by dividing the operational process into several interception stages. In order to solve this problem, the multi-stage S–WTA problem was decomposed into two combat resource assignment subproblems. Firstly, a knowledge-based incremental constructive heuristic was proposed to solve the multi-stage weapon-target assignment subproblem. With the obtained weapon-target assignment scheme, a marginal-loss-based constructive heuristic was proposed to solve the multi-stage sensor-target assignment subproblem. Thus, we can obtain valid solutions of the multi-stage S–WTA problem by incorporating the proposed two fast constructive heuristic algorithms with low complexity. A random sampling method based on random permutations(RP) was employed as the competitor, and some simulation experiments were carried out to validate the effectiveness of the proposed heuristic. The computational result indicates that the proposed heuristic outperforms its competitor for most of the test instances, in terms of both solution quality and time cost.
作者
王艺鹏
辛斌
陈杰
WANG Yi-peng;XIN Bin;CHEN Jie(School of Automation,Beijing Institute of Technology,Beijing 100081,China;Key Laboratory of Intelligent Control and Decision of Complex Systems,Beijing Institute of Technology,Beijing 100081,China;Beijing Advanced Innovation Center for Intelligent Robots and Systems,Beijing Institute of Technology,Beijing 100081,China)
出处
《控制理论与应用》
EI
CAS
CSCD
北大核心
2019年第11期1886-1895,共10页
Control Theory & Applications
基金
国家优秀青年科学基金项目(61822304)
国家自然科学基金项目(61673058)
国家自然科学基金创新研究群体科学基金项目(61621063)
国家自然科学基金重大国际(地区)合作研究项目(61720106011)
NSFC-浙江两化融合联合基金项目(U1609214)
鹏城实验室资助~~
关键词
联合分配
传感器–武器–目标分配
启发式算法
协同作战
Co-allocation
sensor-weapon-target assignment
heuristic algorithms
cooperative engagement
