数据立方体关联挖掘在红外抗干扰中的应用

为了分析空空导弹在红外抗干扰过程中, 各干扰量与脱靶量之间的耦合关系, 利用红外抗干扰试验仿真平台, 通过设置干扰量获得海量数据, 构建数据立方体对海量数据进行数据预处理, 同时统计出数据集中各事务的支持度计数, 将处理后的数据利用FP-Growth关联规则算法挖掘干扰量与脱靶量之间的关联规则。通过对关联规则的分析得到:导弹进入角与弹目距离是影响导弹脱靶量大小的主要因素, 各个干扰量之间不能随意组合, 应该根据导弹来袭方向、目标机动方式选择诱饵投掷策略, 否则干扰效果会大大下降。同时设置一组对比试验, 验证基于数据立方体的FP-Growth算法处理高维数的脱靶量数据, 拥有较高的挖掘效率。 

Application of data cube association mining in infrared anti-jamming

This study aims at analyzing the coupling relationship between disturbance variables and miss distance of air-to-air missiles in infrared anti-jamming. Massive data are obtained by setting disturbance variables through an experimental simulation platform. The data cube is then constructed to preprocess the massive data and compute the support count of each transaction. The processed data are used to mine the association rules between disturbance variables and miss distance by FP-Growth algorithm. Analysis of the association rules show that the entry angle of the missile and the distance between the missile and the target are the main factors for miss distance. The disturbance variables cannot be combined freely, but should be selected according to the attack direction of the missile and the target maneuver mode. Otherwise, the jamming effect will be greatly reduced. A group of comparative experiments is also set up to verify the high efficiency of FP-Growth algorithm based on the data cube in processing the miss distance data with a high dimension.