摘要
对作战目标的识别是指挥与控制系统在作战环境中实施精确打击的前提条件,然而多种系统协同作战必然会对作战目标的识别有差异。针对冲突识别信息融合导致的目标识别不明确甚至错误的问题,提出一种基于改进DS证据理论的指控系统信息融合方法,该方法构建基于智联体的融合模型,在此模型基础上根据皮尔逊相关系数计算出目标识别信息的相关性权重,引入分类思想并结合模糊熵理论计算出目标分类权重,最后用DS融合规则对经过相关性权重和目标分类权重修改后的目标识别信息进行融合。仿真结果表明,与DSCR方法、CRET方法、CBF方法、CMCE方法和NDCM方法相比,所提方法在各种冲突情况下降低了识别错误目标的概率,提高了对正确目标的识别,有效提升了系统的目标识别能力。
The identification of combat targets is a prerequisite for the command and control system to implement precision strikes in the combat environment.However,coordinated operations of multiple systems will inevitably have differences in the identification of combat targets.Aiming at the problem of unclear or even errors in target recognition caused by the fusion of conflict recognition information,the information fusion of command and control system method based on an improved DS evidence theory is proposed.A fusion model based on the intelligent alliance is firstly built,and then according to the Pearson correlation coefficient based on this model,the correlation weight of the target recognition information is calculated,the classification idea is introduced and the fuzzy entropy theory is combined to calculate the target classification weight,and the DS fusion rule is used to fuse the target recognition information after the correlation weight and target classification weight are modified.The simulation results show that compared with the DSCR method,CRET method,CBF method,CMCE method and NDCM method,the proposed method reduces the probability of identifying the wrong targets in various conflict situations,improves the recognition of the correct targets,and effectively improves the target recognition ability of the system.
作者
刘庆利
王文广
张学翠
商佳乐
LIU Qing-li;WANG Wen-guang;ZHANG Xue-cui;SHANG Jia-le(Key Laboratory of Communication and Network,Dalian University,Dalian 116622,China;North Automatic Control Technology Institute,Taiyuan 030006,China)
出处
《火力与指挥控制》
CSCD
北大核心
2022年第2期82-87,共6页
Fire Control & Command Control
基金
国家自然科学基金资助项目(61571074)。
关键词
指挥与控制系统
信息融合
皮尔逊相关系数
模糊熵
DS
融合规则
command and control system
information fusion
pearson correlation coefficient
fuzzy entropy
DS fusion rule
