A Sparse Reconstruction Algorithm for Radar Imaging Based on Bregman Iteration
Xing Zeng, Huaqing Li, Qihua Tian, Zelin Gao, Qingguo Lü, Xuefeng Chen · 2025
The imaging quality of traditional radar imaging algorithms is limited in complex scenarios. To address this issue, this study introduces the Bregman iteration algorithm into the field of radar imaging and proposes a radar imaging algorithm based on Bregman iteration. This algorithm is based on the compressed sensing theory. It conducts sparse processing on radar signals, constructs an appropriate measurement matrix, and utilizes Bregman iteration for sparse reconstruction. Through simulation experiments, in the scenario of 77.9GHz millimeter-wave radar signal processing, multiple targets and Gaussian white noise interference are set. The results show that this algorithm can effectively suppress signal interference, reduce cluttered information in imaging, improve the purity of the signal and the imaging quality, and retain the key information of the targets. It provides a new optimization solution for the development of radar imaging technology.