Association Rule Mining for the Infrared Countermeasure by the PF-Growth Algorithm
| Autor: | Wu Youli, Fang Yangwang, Zhang Danxu, Xu Yang, Huang Chen |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
010309 optics
Countermeasure Association rule learning Computer science Heuristic 0103 physical sciences 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing 02 engineering and technology 01 natural sciences Algorithm Data modeling |
| Zdroj: | 2018 37th Chinese Control Conference (CCC). |
| DOI: | 10.23919/chicc.2018.8483222 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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