Scanning Radar Angular Superresolution for Low SNR Applications by Modified Combination Regularization Norms Approach

Regularization algorithms with combination regularization terms have an advantage at improving the angular resolution of the forward-looking scanning radar. Especially, the combination of L<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> regularization constraint and total variation (TV) regularization constraint can simultaneously improve the angular resolution and achieve clear outline reconstruction performance. However, the ill-posed feature of convolution matrix leads to serious noise amplification phenomenon in the superresolution imaging procedure. In this paper, an modified combination regularization norms (M-CRN) approach is proposed to improve the angular resolution of forward-looking scanning radar when the echo signal-to-noise ratio (SNR) is low. First, based on the L<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> regularization norm and TV regularization norm, the forward-looking scanning radar superresolution problem is modeled as a combination regularization norms problem. Second, the iterative re-weighted least square (IRLS) method is applied to obtain the optimized solution of the objective function. Finally, in the iterative procedure, the convolution matrix is repaired through the truncated singular value decomposition (TSVD) strategy, which can mitigate the ill-posed feature of convolution matrix, resulting in the combination regularization terms approach can be used in the low SNR applications. Simulations are carried out to demonstrate the proposed M-CRN approach.