RMA-Based Azimuth-Range Decouple Method for Automotive SAR Sparse Imaging
Yan Zhang, Jie Zhao, Bingchen Zhang, Yirong Wu
Abstract
Synthetic aperture radar (SAR) technology is investigated for automotive applications in recent years. Sparse imaging methods have the potential to improve the quality of SAR images at the expense of more computation and larger memory. To avoid the huge memory and computational costs, traditional range migration algorithm (RMA) based azimuth-range decouple operator is adopted in this article. We introduce RMA operators to automotive SAR sparse imaging. In each iteration of sparse reconstruction, RMA based operators are used to approximate the multiplication of sensing matrix. Compared with conventional RMA method, the proposed method effectively enhances the resolution and suppresses background noise of automotive SAR images with only dozens of times of computational costs and two times of memory. Simulations and real-data experiments are carried out to verify the feasibility and effectiveness of the proposed method.