Multi-Domain Feature Fusion Based Radar Deception Jamming Recognition Method
Xi Yu, Wantian Wang, Hao Wu, Juntian Bo, Jiahao Zhang, Jin Meng · 2023
Considering the challenges posed by low recognition probability, high computational complexity, and difficult engineering implementation in radar deception jamming recognition under a low jamming-to-noise ratio (JNR), a method for recognizing radar deception jamming based on multi-domain feature fusion is proposed. This method analyzes the mathematical models of three types of deception jamming-intermittent sampling repeater jamming (ISRJ), dense false target jamming (DFTJ), and distance deception jamming (DDJ) -to extract two features: envelope fluctuation and waveform similarity in the time and frequency domains, respectively. Jamming classification and recognition are achieved using a multi-class support vector machine (multi-class SVM) model. Simulation results demonstrate that as JNR increases, there is a gradual improvement in the recognition rate which reaches 90%at ldB.