Active Deception Jamming Recognition in the Presence of Extended Target

Accurate sensing of the mainlobe active deception jamming is critical for radar antijamming and extended target detection in a complex electromagnetic environment. This letter, therefore, deals with the problem of multiple active deception jamming recognition in extended target settings. A residual convolutional neural network (CNN) with an attention mechanism-based radar active deception jamming recognition algorithm is proposed, leveraging a hybrid model to capture many rich features through multidomain feature fusion. The proposed method can outperform state-of-the-art methods in terms of recognition accuracy, model size (MS), and convergence speed. Experimental results demonstrate its effectiveness and robustness.