Abstract: Radar plays a vital role in modern electronic warfare,and as the contest between electronic countermeasures and electronic resistance intensifies,the problem of difficult manual extraction of features for active radar interference and low recognition rates under low JNR in complex electromagnetic environments needs to be addressed urgently.This paper proposes an interfe-rence recognition algorithm based on the parallelization of multiscale and fully convolutional neural network(MFCN) and gated recurrent unit(GRU) to solve the above problem.This is an end-to-end deep neural network model,which does not require complex pre-processing of the data,and the original time domain sequence of the interference signal is input to classify and identify the interference signal under different JNR.Simulation results show that the recognition accuracy of the network gradually increases as the JNR gradually increases;the overall recognition rate of the network is 99.4% in the full JNR range of －10 to 10 dB,and the recognition accuracy is close to 100% when the JNR is above －6 dB,which has a higher recognition accuracy compared with the simple multiscale and fully convolutional neural network,gated recurrent unit and other classical models,and the limit of the adaptive JNR is lower.

Key words: Radar active jamming, Interference recognition, Time domain sequence, Deep learning, Feature fusion