Association Rule Mining for the Infrared Countermeasure by the PF-Growth Algorithm

To explore the main influence factors in the infrared countermeasure and reveal the effects of the combinations of the influence factors, this study provides a heuristic idea by adopting the association rule mining theory. First of all, an engagement model including the target model, flare model and...

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Bibliographic Details
Main Authors: Yang, Xu, Yang-Wang, Fang, You-Li, Wu, Dan-Xu, Zhang, Chen, Huang
Format: Conference Proceeding
Language:English
Subjects:
Acceleration
Aircraft
Analytical models
association rule mining
Data mining
Data models
FP-growth algorithm
Infrared countermeasure
Itemsets
miss distance
Missiles
overlap effect
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Summary:To explore the main influence factors in the infrared countermeasure and reveal the effects of the combinations of the influence factors, this study provides a heuristic idea by adopting the association rule mining theory. First of all, an engagement model including the target model, flare model and missile model is constructed to show different attack situations and countermeasure modes. Meanwhile, a counter-countermeasure algorithm denoted overlap effect is proposed as a recognition approach for distinguishing the true target from the target-flare mixed signal. Then, in view of the miss distance, we separate the association rules into outer and inner levels for mining the relations between the miss distance and the countermeasure factors. Afterwards, FP-growth algorithm is introduced to unearth the association rules by using the off-line data. Finally, we thoroughly investigate the association rules and disclose the main influence factors through simulation examples.
ISSN:2161-2927
DOI:10.23919/ChiCC.2018.8483222