Intelligent classification and identification of radar jamming signals

Abstract

Aiming at the problem of intelligent classification and recognition of radar jamming signals, the convolutional neural network structure is studied. By optimizing the basic network, the normalization layer and activation layer is added to the LENET-5 structure to improve the accuracy of recognition results. The linear frequency modulation signal and amplitude modulation interference, frequency modulation interference, comb spectrum interference, slice reconstruction interference, intermittent sampling and forwarding interference are analyzed. Six signal models are used to generate data sets, and intelligent methods are adopted to realize classification and recognition.