多阶段传感器-武器-目标分配问题的建模与优化求解

Based on the static sensor - weapon - target assignment (S - WTA) problem, this paper divides the combat process into multiple interception stages, aims to minimize the expected value of the remaining threat of the incoming targets, and establishes a multi - stage S - WTA problem model. To solve this problem, this paper decomposes the multi - stage S - WTA problem into two types of combat resource allocation sub - problems. Firstly, a knowledge - based incremental constructive heuristic algorithm is proposed to solve the multi - stage weapon - target assignment sub - problem. According to the determined multi - stage weapon - target assignment scheme, a marginal - loss - based constructive heuristic algorithm is proposed to solve the multi - stage sensor - target assignment sub - problem. Combining two low - complexity and fast constructive heuristic algorithms realizes the effective solution of the multi - stage S - WTA problem. This paper selects the random sampling algorithm based on random permutation (RP) as a comparison algorithm and verifies the effectiveness of the algorithm through simulation experiments. The experimental results show that the algorithm proposed in this paper is superior to the RP algorithm in terms of the solution quality and time cost for most examples.

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.