摘要
针对目标编群中单一算法存在的适用范围小、误分率高的问题,提出一种新的态势估计中目标编群的处理方法。首先应用Hop fie ld神经网络对态势中目标的目的地做出判断,然后采用多相似性加权策略计算出目标间的相关系数,再根据最大相关系数层次聚类算法实现编群。仿真结果表明方法能在一定程度上减小错误编群的概率,同时适用范围也得到了扩展。
Towards the limited range and high error rate of the traditional target grouping method,we present a novel model to deal with the target grouping problem.Firstly,A Hopfield neural network is selected to resolve the pre-destination of the targets;then,the weighted similarity measure is used to calculate the relative value;thirdly,the target clustering is implemented by the hierarchical clustering algorithm.The result indicates that the method can reduce the error rate of clustering and enlarge the available area.
出处
《火力与指挥控制》
CSCD
北大核心
2011年第9期69-72,76,共5页
Fire Control & Command Control
关键词
态势估计
目标编群
HOPFIELD神经网络
多相似性加权
层次聚类
situation assessment
target grouping
Hopfield neural networks
weighted similarity measure based on multi-clustering
hierarchical clustering method
