Research on Spatial Angle Super-Resolution Algorithm Based on Adaptive Layered Sparse

Compressed Sensing (CS) theory is based on the sparsity of signals and involves compressively sampling high-dimensional data to obtain a small number of linear observations that contain the complete information of the signals. By solving an optimization problem, the original signals can be recovered from these compressed linear observations. This paper combines CS theory with the sparse characteristics of targets in the spatial domain and proposes an adaptive layered sparse spatial angle super-resolution algorithm. This algorithm converts the target spatial angle super-resolution into the problem of using orthogonal basis to reconstruct sparse signals. Then, the target spatial angle super-resolution is carried out by using single snapshot data through optimization solution, and the target spatial angle domain is continuously reduced through layered orthogonal matching pursuit (LOMP) solution to complete the target spatial angle reconstruction. The effectiveness and robustness of the algorithm are verified by using the proposed algorithm to process the measured data.