A sparse sampling strategy for angular superresolution of real beam scanning radar

This paper investigates techniques for angular superresolution using limited data of real beam scanning radar (RBSR). In order to improve the angular resolution of RBSR, many algorithms have been proposed. However, for most algorithms, large amounts of sampling data is necessary. The requirement of data increases the burden of the radar system. Fortunately, the sparse signal reconstruction techniques provide a new train of thought for us. It has been proved in array signal processing and image processing that the techniques only need limited sampling data to realize DOA estimation and image superresolution. This paper describes the sparse sampling model of RBSR as an underdetermined equation-solving problem, the received signals are sparsely recovered in target domain. Two algorithms, including smooth approximation algorithm and focal underdetermined system solver (FOCUSS), based on different optimization ideas, are adopted to solve the problem. Simulation results show that compressive sampling methods can recover the target domain accurately, especially under the condition of high signal-to-noise ratio (SNR).