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… Click to show full abstract
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.
               
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