Jamming Recognition Based on AC-VAEGAN

To solve the performance deterioration of jamming recognition method based on deep learning in the case of small sample set, a jamming recognition method based on AC-VAEGAN is presented. Interference signal time-frequency diagrams are used as the small sample training set. AC-VAEGAN network is designed by modifying the structure of ACGAN network, that is, adding the core idea of VAE in the ACGAN network, so that the latent space of a continuous and meaningful sample set can be obtained through the encoder. By combining of regularization loss, reconstruction loss, source loss and classification loss, loss functions of three parts of AC-VAEGAN are obtained, and the three parts are trained to make the AC-VAEGAN network more efficient. The simulation results show that when the jamming-to-noise ratio is -10dB~10dB, the AC-VAEGAN network has a higher correct recognition rate for the five kinds of jamming than ACGAN and CNN networks in the case of small sample datasets.