| Citation: | WU Xin, WU Youli, NIU Deqing, et al. Application of data cube association mining in infrared anti-jamming[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(11): 2304-2313. doi: 10.13700/j.bh.1001-5965.2021.0096(in Chinese)
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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.
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