EFFICIENT NONCONVEX REGULARIZATION FOR AZIMUTH RESOLUTION ENHANCEMENT OF REAL BEAM SCANNING RADAR

Azimuth superresolution for real beam scanning radar aims to recover the high-resolution image from low-resolution echo. Among superresolution techniques, regularization-based methods are widely used, but most existing methods lead to the blurring of scattering targets and thus are difficult to distinguish between close targets. In this paper, we propose to employ the nonconvex l(p)-regularization with 0 < p < 1 to achieve the sparsity-driven superresolution, which further enhances the azimuth resolution. Furthermore, the resultant optimization problem is efficiently solved using an unified framework via incorporating different proximity operators. Simulation results validate the accuracy and efficiency of the proposed algorithm.