Toward Small-Sample Radar Target Recognition via Scene Reimaging

Target recognition via deep learning has achieved great performances in the preceding works. Yet this family of method are dependent on large amounts of training data with label information. For radar sensors, it is difficult to collect labeled data in practical due to the absent imaging truth. The commonly used solution is data augmentation. However, seldom studies are devoted to complex-valued radar images. In this article, a new radar image generation method via target reimaging is proposed. The original image is first cast into the frequency-aspect domain. The one axis represents the transmitted frequency, while the other presents the synthetic azimuth. The inverse operations of zero-padding and windowing are then applied on the transformed data. Two kinds of imaging techniques, the intrasample reimaging and the intersample reimaging are presented to generate new radar images. The generated images are finally used to improve the learning effectiveness of deep models. Multiple comparative studies are performed to demonstrate the advantages of the proposed method.