Double Sparse Structure-Enhanced mmWave NLOS Imaging Under Multiangle Relay Surface

Non-line-of-sight (NLOS) mmWave imaging technology reconstructs the contour features of hidden targets by analyzing the indirect reflected signals of the relay surface, which has been a hot topic in disaster reserve and autonomous driving. However, due to the differences in the reflecting characteristics of multiangle relay surfaces, traditional multipath utilization methods inevitably suffer from disturbance, and obtaining high-quality images remains a challenging task. In this paper, we propose a double sparse structure enhanced mmWave NLOS imaging framework. First, we establish an automotive-squint synthetic aperture radar (AS-SAR) model under multiangle relay surface and analyze the multiangle image characteristics. Subsequently, we introduce a double sparse structure to transform the image reconstruction problem into a hybrid convex regularization problem, and theoretically derive the minimum lower bounds of sample complexity and estimation error. Then, based on the fast iterative threshold shrinkage framework, we propose a time-domain double sparse thresholding algorithm (TD-DSTA), in which the double sparse operator is optimized by alternating direction multiplication. In addition, we propose a two-dimensional frequency domain method based on the approximate-operator to reduce the computational complexity. Finally, we evaluate the performance of the proposed method through quantitative and qualitative analysis in the NLOS multiangle relay surfaces scenario. Simulation and real experimental results verify the superiority of the proposed method in NLOS image reconstruction.