Radar active jamming recognition based on time-frequency image classification

Flexible active radar jamming has become one of the main threats in modern electronic warfare. Aiming at the problem of classifying and identifying active jamming patterns of towed decoy radar, this paper proposes a set of methods for classifying and identifying jamming time-frequency images based on deep learning. The content mainly includes: using short-time Fourier transform to obtain time-frequency images of six kinds of active interference under different interference signal ratios. Using ResNet network to realize the classification and recognition of different interferences. Through simulation analysis, the algorithm can still obtain better results under low interference-to-noise ratio.