Intelligent Recognition Method of Radar Active Jamming Based on Parallel Deep Learning Network

The traditional radar active jamming recognition is a pattern recognition method by manually extracting multiple feature parameters and sending them to a predetermined classifier for classification. This method has some problems, such as sensitive characteristic parameters to jamming patterns and not high recognition accuracy, etc. In response to the above problems, an intelligent recognition method for radar active interference based on deep learning is proposed in this paper, and a new network structure in which a residual network and a Long short-term memory network are connected in parallel is designed. In addition, this method is based on the joint processing of multi-dimensional information, which can improve the robustness of interference recognition. The recognition performance of six kinds of conventional radar active jamming patterns is verified by field experiments, and the recognition accuracy rate reaches 94. 80%, this proves that the proposed method has a good engineering application prospect.