A Sparse Denoising-Based Super-Resolution Method for Scanning Radar Imaging

Scanning radar enables wide-range imaging through antenna scanning and is widely used for warning. The Rayleigh criterion indicates that narrow beams of are required to improve the azimuth resolution. However, a narrower beam means larger aperture. In practical applications, due platform limitations, aperture limited, resulting in low conventional sparse super-resolution method (SSM) has been proposed improving resolution achieving superior performance. This uses L1 norm represent prior target solves regularization problem achieve under framework. strong-point targets improved efficiently. some with typical shapes, strong sparsity treats them as targets, loss shape characteristics. Thus, we can only see points its processing results. applications need identify detail, SSM lead false judgments. this paper, denoising-based (SDBSM) compensate deficiency traditional SSM. SDBSM minimization scheme denoising, which helps reduce influence noise. Then, achieved by alternating iterative denoising deconvolution. As rather than deconvolution, constraint reduced. Therefore, it effectively preserve while performance was demonstrated via simulation real data