Radar Forward-looking Super-resolution Imaging Method Based on Sparse and Low-rank Priors

Radar forward-looking imaging is important in many fields, such as precision guidance, autonomous landing, and terrain mapping. Due to the constraints of actual radar aperture, obtaining high-resolution images using the traditional forward-looking imaging method based on real beam scanning is challenging. Compared with the entire imaging scene, the objects of interest usually occupy only a small part of the area. This sparsity enables the use of Compressed Sensing(CS) to reconstruct high-resolution forward-looking images. However, the high noise in the radar echo affects the quality of the image generated by the compressed sensing method. Inspired by the low-rank property of the final image, this paper proposes a forward-looking super-resolution imaging model that combines sparse and low-rank properties. To effectively solve the dual constraint optimization problem in the proposed model, a forward-looking image reconstruction method based on an Augmented Lagrange Multiplier(ALM) within the framework of the Alternating Direction Multiplier Method(ADMM) was proposed. Finally, the experimental results from simulation and real data show that the proposed method can effectively improve the azimuth resolution of radar forward-looking imaging while also being noise-robust.