Torpedo electromagnetic fuze active interference recognition based on improved AlexNet

In order to enhance the active interference signal recognition capability of the electromagnetic fuze of a torpedo in the marine electromagnetic environment, a method for recognizing the electromagnetic fuze's active interference based on the improved AlexNet is proposed. Interference signal models and marine electromagnetic wave channel models are established. The time-frequency plots of interference signals propagated through the marine electromagnetic wave channel are extracted into datasets. The trained network model is improved by optimizing the network structure, adding data labels and augmenting data. 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 a high interference recognition rate under the low JNR condition, accurately recognizes the electromagnetic fuze's active interference and exhibits low model complexity.