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.