Torpedo Electromagnetic Fuze Active Interference Recognition Based on Improved AlexNet

In order to enhance the recognition capability of active interference signals from torpedo electromagnetic fuze in the marine electromagnetic environment, a method for recognizing torpedo electromagnetic fuze active interference based on improved AlexNet is proposed. Interference signal models and marine electromagnetic wave channel models are established, and the time-frequency plots of the interference signals propagated through the marine electromagnetic wave channel are extracted as the dataset. By optimizing the network structure, adding data labels, and employing data augmentation techniques, the trained network model is improved. The recognition rate and complexity of the model under different Jamming-to-Noise Ratio (JNR) conditions are provided. Simulation results demonstrate that the proposed method achieves high interference recognition rates under low JNR conditions, accurately identifies torpedo electromagnetic fuze active interference, and exhibits low model complexity.