基于耗散结构理论的极大熵目标分群算法

为快速准确实施战场目标分群,从信息交互角度,结合耗散结构理论将战场态势认为是能够出现"有序" 状态的动态开放系统.据此,建立用于战场目标分群的OG-Brusselator 模型,并且改进了MEC 算法,在航路勾径基础上提出航路股径概念统一目标彼此间距离和运动方向两要素的量纲,将其和目标与分群中心隶属度分别作为影响目标群属关系明晰度的可量化正、负熵指标, 提出以OG-Brusselator 模型控制算法迭代的目标分群算法DS-MEOC.最后针对空中目标进行分群实验分析,结果表明DS-MEOC 算法有效可行,相比MEC算法,能够提供更合理的目标分群方案.

Maximum entropy object grouping algorithm based on dissipative structure theory

To group battle object rapidly and exactly, consider battle situation as a dynamic open system in terms of information exchange and with the dissipative structure theory, in which "ordered" status could appear. On these grounds, established the OG-Brusselator mode for battle object grouping and improved the algorithm MEC, then proposed the concept of fairway lengthways-cut based on the fairway crosscut to unify dimensions of two elements: the distance between objects and their direction of movement, and set them together with the memberships of object to grouping center as the measurable positive and negative entropy indicators respectively that affecting the clarity of relationship between object and group, then proposed object grouping algorithm DS-MEOC using OG-Brusselator to control the iteration. The experiment and analysis of air targets grouping proved that DS-MEOC is effective, feasible and could provide more reasonable object grouping program as compared with MEC.