ADAPTIVE SPARSE ITERATIVE REWEIGTHED SUPER-RESOLUTION METHOD FOR SCANNING RADAR IMAGING

Recently, a sparse super-resolution method relying on L-1 iterative reweighted norm (IRN) has been proposed to improve the imaging resolution of scanning radar. However, the method has poor adaptability due to the noise-sensitive user-parameter. To this end, an adaptive L-1 iterative reweighted sparse super-resolution method with no user-parameter is derived. Firstly, the scanning radar super-resolution model is established. Secondly, the user-parameter selection in the L-1-IRN method is analyzed. Finally, the adaptive iteration weights are derived by transforming the sparse estimation problem into a maximum posterior (MAP) estimation problem. Compared with the existing L-1-IRN method, the proposed method does not have any user-parameter, so it has adaptability to different signal-to-noise ratios (SNR) and is more robust. Simulation verifies the superiority of the proposed method.