RDJCNN: A micro-convolutional neural network for radar active jamming signal classification

Timely awareness of jamming situations and classification of jamming categories are vital for radars to suppress jamming, ensure viability, and maintain functions in complex electromagnetic environments. To satisfy strict time requirements for radars on embedded devices, a micro-dynamic convolutional neural network for jamming signal classification is proposed in this paper. The proposed network takes the range-Doppler distribution obtained from built-in radar signal processing as input. The proposed data augmentation algorithm, together with the attention mechanism and the efficient convolutional architecture, improves the generalization capability and reduces the computational complexity. In addition, we propose a dynamic depth mechanism based on a task difficulty evaluator that enables the network to be adjusted automatically and further reduces the average computational complexity of classification. Simulation results verify the advantages of our approach in size, accuracy, and efficiency. The proposed network achieved 98.82% and 85.00% top-1 accuracy in two datasets with only 1.73 M multiply-accumulate operations.