Forward-Looking Radar Super-Resolution Imaging Combined TSVD with L1 Norm Constraint

This paper is devoted to the research of methods and experiments of regularization deconvolution theory on the azimuth super-resolution of forward-looking imaging radar. L1 norm is usually used as a regular term to obtain a stable solution due to its strong resolving power for sparse targets. However, deconvolution is an ill-posed problem, in the process of deconvolution iteration, using the L1 norm as a regular term is sensitive to noise and may causes a large deviation between the solution and the true value due to the influence of noise. This paper proposes the super-resolution imaging method combined truncation singular value decomposition (TSVD) with L1 norm constraint. At lower SNR, this method effectively solves the problem of noise amplification during deconvolution iteration. The effectiveness and advancement of the proposed algorithm are verified by simulation results.