One-Shot HRRP Generation for Radar Target Recognition

Abstract

Insufficient data of a noncooperative target seriously affect the performance of radar automatic target recognition (RATR) using the high-resolution range profile (HRRP), especially when the noncooperative target has only one sample. To this end, we propose an unsupervised data generation method to generate noncooperative HRRP signals. We utilize the pretrained generative adversarial networks (GANs) model to learn the HRRP general probability distribution. To emphasize the representative and discriminative power of generated HRRP signals, a joint optimization method is proposed to preserve category information. Moreover, a feature diversification method is proposed to make the generated samples have sufficient aspect characteristics to further fit the probability distribution of the noncooperative target. Thus, the generated HRRP signals can effectively improve the recognition performance of noncooperative target. Extensive experiments on HRRP data sets demonstrate the superior performance of our method over other state-of-the-art methods.