Radar Target Recognition Algorithm Based on Data Augmentation and WACGAN with a Limited Training Data

At present, the existing high-resolution range profile radar target recognition algorithm with limited training data still has several drawbacks (e. g., low recognition accuracy and poor recognition stability). To this end, an efficient radar target recognition algorithm is developed in this paper, which is based on data augmentation and Weighted Auxiliary Classifier Generative Adversarial Networks (WACGAN). Specifically, we expand data set by using the data augmentation method based on time mirroring, and the radar target scattering characteristics is considered. After that, the WACGAN with expanded data set is used to automatically select high-quality generated samples and further optimize the discriminator, which has been improved through the supervised learning. Then, the optimized discriminator is used to recognize radar target. Finally, several numerical experiments have been carried out to demonstrate that, under the condition of limited training data, the proposed algorithm possesses higher recognition accuracy and better recognition stability without increasing recognition time. © 2020, Chinese Institute of Electronics. All right reserved.