A jamming identification method based on deep learning for networking radars

Jamming identification is the premise of radar anti-jamming in the complex electromagnetic environment. The signals from monostatic radar are taken as the object of training and identification, which has the disadvantages of less information, single observation angle and easy to be attacked. In order to improve the identification accuracy, a jamming identification method based on deep learning for networking radars is proposed in this paper. The range-Doppler signals from multiple radars in the network are stitched into a data set for jamming identification, which contains more information than that from monostatic radar. The models of radar jammings are established, and a Convolutional Neural Network is designed to identify jammings, target signal and noise. The simulation results show that the accuracy of the proposed jamming identification method is 99.2%.