The dynamic weapon-target assignment （DWTA） problem is an important issue in the field of military command and control. An asset-based DWTA optimization model was proposed with four kinds of constraints considered, ...The dynamic weapon-target assignment （DWTA） problem is an important issue in the field of military command and control. An asset-based DWTA optimization model was proposed with four kinds of constraints considered, including capability constraints, strategy constraints, resource constraints and engagement feasibility constraints. A general ＂virtual＂ representation of decisions was presented to facilitate the generation of feasible decisions. The representation is in essence the permutation of all assignment pairs. A construction procedure converts the permutations into real feasible decisions. In order to solve this problem, three evolutionary decision-making algorithms, including a genetic algorithm and two memetic algorithms, were developed. Experimental results show that the memetic algorithm based on greedy local search can generate obviously better DWTA decisions, especially for large-scale problems, than the genetic algorithm and the memetic algorithm based on steepest local search.展开更多
针对指挥与控制系统在目标识别过程中的模糊特征信息导致目标识别准确率低的问题,提出基于多属性决策的模糊特征目标识别方法。首先,引入三角模糊数,将观测值转化为模糊数;然后,使用加权欧式距离计算目标相似度,并使用基于指标相关性的...针对指挥与控制系统在目标识别过程中的模糊特征信息导致目标识别准确率低的问题,提出基于多属性决策的模糊特征目标识别方法。首先,引入三角模糊数,将观测值转化为模糊数;然后,使用加权欧式距离计算目标相似度,并使用基于指标相关性的权重确定(criteria importance through intercriteria correlation,CRITIC)方法得到目标特征属性权重,基于目标相似度和特征属性权重构建加权相似度矩阵;最后,使用相对熵排序法计算出目标贴近度排序,识别出目标。基于多个指标的仿真结果表明,所提方法提高了目标识别准确率和系统的目标识别能力。展开更多
基金Supported by the National Natural Science Foundation of China (Grant No. 60374069)the Foundation of the Key Laboratory of Complex Systems and Intelligent Science, Institute of Automation, Chinese Academy of Sciences (Grant No. 20060104)
文摘The dynamic weapon-target assignment （DWTA） problem is an important issue in the field of military command and control. An asset-based DWTA optimization model was proposed with four kinds of constraints considered, including capability constraints, strategy constraints, resource constraints and engagement feasibility constraints. A general ＂virtual＂ representation of decisions was presented to facilitate the generation of feasible decisions. The representation is in essence the permutation of all assignment pairs. A construction procedure converts the permutations into real feasible decisions. In order to solve this problem, three evolutionary decision-making algorithms, including a genetic algorithm and two memetic algorithms, were developed. Experimental results show that the memetic algorithm based on greedy local search can generate obviously better DWTA decisions, especially for large-scale problems, than the genetic algorithm and the memetic algorithm based on steepest local search.
文摘针对指挥与控制系统在目标识别过程中的模糊特征信息导致目标识别准确率低的问题,提出基于多属性决策的模糊特征目标识别方法。首先,引入三角模糊数,将观测值转化为模糊数;然后,使用加权欧式距离计算目标相似度,并使用基于指标相关性的权重确定(criteria importance through intercriteria correlation,CRITIC)方法得到目标特征属性权重,基于目标相似度和特征属性权重构建加权相似度矩阵;最后,使用相对熵排序法计算出目标贴近度排序,识别出目标。基于多个指标的仿真结果表明,所提方法提高了目标识别准确率和系统的目标识别能力。
