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In regards to electromagnetic warfare application, this work proposes a radar intrapulse recognition algorithm based on bicubic interpolation Wigner–Ville distribution (WVD). The proposed method is aimed at overcoming the complexity of traditional feature extraction and enhancing the robustness of intelligent intrapulse recognition systems. First, we convert the WVD matrices of radar intrapulse signals into square matrices using bicubic interpolation. This enables the intrapulse recognition algorithm to handle radar signals with varying lengths. Second, we use the square matrices directly to train a convolutional neural network (CNN), rather than first saving them as images and then training the network with those images. This is because automatic storage and retrieval of images can sometimes alter the numerical values in the matrices. We enhance the performance of the CNN by utilizing batch normalization and one-hot encoding techniques, and continuously evaluate the CNN during training to save the best performing model. Finally, we use the trained CNN to identify the intercepted radar intrapulses and verify the reliability of the proposed method based on the radar signals generated by hardware devices. The experimental results demonstrate that the proposed algorithm can recognize radar intrapulses with varying pulsewidths. Furthermore, the proposed algorithm exhibits strong recognition performance even at a low signal-to-noise ratio and has lower time complexity than the existing algorithms.

Intrapulse Recognition of Radar Signals Via Bicubic Interpolation WVD