摘要
战场环境复杂多样,各种探测手段层出不穷,空中威胁属性指标种类繁多,增大了指挥员对空中态势威胁分析难度。正确、快速地对空中态势进行威胁分析,将给战场部署提供有效的决策依据。建立基于K-Means聚类算法的空中目标威胁等级聚类模型,通过对空中目标威胁属性特征的数据进行分析,对威胁目标聚类进行深度挖掘,将目标威胁等级问题转化为最优聚类问题。实例分析表明该算法在对威胁目标等级聚类中有效,提高了目标威胁等级聚类的可靠性、精确性。
The battlefield environment is complex and diverse and various detection methods emerge in an endless stream. There are various types of air threat attribute indicators,which increase the difficulty of the commander to analyze the threat situation in the air.Analysis of the air threat situation correctly and rapidly will provide effective decision-making basis for battlefield deployment.This paper builds an airborne target threat hierarchical clustering model based on K-Means clustering algorithm. Through the analysis of the data of the air target threat attribute,the deep mining of threat target clustering is conducted and the target threat level problem is transformed into the optimal clustering problem. The example analysis shows that the algorithm is effective in clustering the threat target level and improves the reliability and accuracy of the target threat level clustering.
作者
谷玉荣
黄耀雄
高艳
郭静
GU Yu-rong;HUANG Yao-xiong;GAO Yan;GUO Jing(North Automatic Control Technology Institute,Taiyuan 030006,China)
出处
《火力与指挥控制》
CSCD
北大核心
2019年第4期92-96,共5页
Fire Control & Command Control
