Superresolution Imaging for Real Aperture Radar by Two-Dimensional GP-SOONE Method

Ground-to-air real aperture scanning radar receives range-azimuth-pitch three-dimensional echoes by antenna scanning, and the echoes of a fixed range bin can essentially be modeled as a two-dimensional (2D) convolution relationship between the target scattering coefficient and the azimuth and pitch 2D antenna pattern. However, the angular resolution of real aperture radar, including pitch and azimuth directions, is limited by its aperture size. In this paper, a robust 2D-gradient projection (GP)-sequential order one negative exponential (SOONE) superresolution imaging algorithm is proposed to enhance the 2D angular resolution. First, the echo signal model of the scanning radar is established. Second, a SOONE function is extended as a 2D case to describe the sparse properties of the targets, and the 2D angular superresolution problem is transformed into a constrained optimization problem. Finally, the GP method is adopted to solve the constrained optimization problem, and we use the regularization strategy to enhance the robustness of the algorithm. Our proposed algorithm is competitive with the existing algorithms in terms of discrimination capability and computational efficiency. The effectiveness of the algorithm is verified by comparing with the simulations of other superresolution algorithms.