Radar active jamming recognition based on time-frequency image classification

Jingyi Wang, Wen Dong, Zhi-yong Song
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Abstract

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.
基于时频图像分类的雷达有源干扰识别
柔性有源雷达干扰已成为现代电子战的主要威胁之一。针对拖曳诱饵雷达有源干扰模式的分类与识别问题,提出了一套基于深度学习的干扰时频图像分类与识别方法。内容主要包括:利用短时傅里叶变换获得六种有源干扰在不同干扰信号比下的时频图像。利用ResNet网络实现对不同干扰的分类识别。通过仿真分析,该算法在低干扰噪声比下仍能获得较好的效果。
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