Recognition-Aware HRRP Generation With Generative Adversarial Network

Abstract

Existing works on radar high-resolution range profile (HRRP) recognition commonly focus on utilizing data and various deep learning models in achieving high classification accuracy. However, in practical applications, it is often difficult to obtain HRRP signals, especially for noncooperative targets. Such lack of data dramatically decreases the recognition performance, so this letter applies data augmentation to address small-sample problems. A recognition-aware HRRP generation framework based on a generative adversarial network is proposed for data augmentation, which generates discriminative samples by decomposing and reorganizing signal’s characteristics. The proposed model increases the generated signals’ discriminative power, thus meeting the application requirements. Experiments show that the generated HRRP signals can not only accurately expand the data set but also improve the recognition system’s performance. Besides, the developed model outperforms traditional data augmentation methods and other generative methods. To the best of our knowledge, this is the first work on HRRP signal generation in radar automatic target recognition systems.