红外对抗中各因素之间关系的深度挖掘  

Depth Mining of the Relationship among the Factors in Infrared Countermeasures

作　　者：陈鞭 伍友利[1] 吴鑫 甘跃鹏 Chen Bian;Wu Youli;Wu Xin;Gan Yuepeng(Air Force Engineering University,Xi’an 710038,China)

机构地区：[1]空军工程大学,西安710038

出　　处：《航空兵器》2022年第3期42-46,共5页Aero Weaponry

摘　　要：为研究红外对抗中各因素之间及其与导弹脱靶量之间的关系,基于FP-Growth算法进行关联规则挖掘。在红外对抗挖掘过程中,用K-Means聚类算法对诱饵投掷时刻、导弹进入角和弹目初始距离等连续因素进行离散化处理,用Kulc和IR指标对得到的关联规则进行深度筛选。两个筛选指标共过滤掉69条效果不佳的关联规则,得到最终规则。结果表明,关联规则挖掘方法在红外抗干扰评估研究中有效可行,可为分析红外对抗问题提供研究思路。In order to analyze the relationship among various factors in infrared countermeasures and their relationship with missile miss distance,association rules are mined based on FP-Growth algorithm.In the process of infrared countermeasure mining,K-means clustering algorithm is used to discretize the continuous factors such as docey throwing time,missile entry angle and initial distance of missile and target.Then,Kulc and IR indexes are used to deeply screen the obtained association rules.Two screening indexes filter out 69 ineffective association rules,and the final rules are obtained.The results show that the association rule mining method is effective and feasible in the research of infrared anti-jamming evaluation,and can provide research ideas for the analysis of infrared countermeasure problems.

关 键 词：红外对抗 聚类离散化 关联规则 Kulc指标 IR指标 诱饵 目标 导弹 

分 类 号：TJ760.1[兵器科学与技术—武器系统与运用工程] TN976[电子电信—信号与信息处理]