In modern military operations, in which electronic warfare plays an increasingly important role, radar jamming and anti-jamming technology is always an important research topic. With the rapid development of digital radio frequency memory technology (DRFM) in recent years, the development of deceptive jamming has made great progress, and numerous jams with good deception effect have been put into the actual battle and play an important role. Most of the current research on the classification of active deceptive jamming has chosen fewer types of jamming and is less generalizable. This paper addresses this problem by selecting a variety of types of spoofing jamming and compound jamming, and after extracting suitable feature parameters, using support vector machines and BP neural networks for classification, the trained classifiers have better performance in terms of accuracy and robustness, as well as generalizability, providing a theoretical basis for engineering applications.
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