Semisupervised Radar Intrapulse Signal Modulation Classification With Virtual Adversarial Training

Radar intrapulse signal modulation classification is an important work for the electronic countermeasure and there are mainly two categories of algorithms. The deep learning-based algorithms usually outperform the traditional feature extraction-based ones, but they may rely on massive labeled samples for training, which limits their practical applications. To solve this problem, the SS-LWCNN model which combines the semisupervised learning (SI-SL) with virtual adversarial training (VAT) and the light weight technology is proposed. VAT provides the proposed model with robustness to the local perturbation of samples, which improves the classification accuracy with limited labeled samples provided. The light weight technology greatly reduces the complexity of the model, which increases the speed of classification. As demonstrated by the simulation results, in the condition of limited labeled samples are available, the SS-LWCNN model obtains greater classification accuracy compared to the other models. As tested by both the white Gaussian noise and the impulsive noise affected signals data sets, the SS-LWCNN model shows stronger robustness than the comparable models. Furthermore, the SS-LWCNN model contains much fewer training parameters and less floating point operations (FLOPs) than the other models.