A regularization imaging method for forward-looking scanning radar via joint L1-L2 norm constraint

Regularization technology can be utilized to improve the azimuth resolution for forward-looking scanning radar (FLSR). Among various regularization methods, L1 norm constrained method is usually adopted for its strong ability in resolving the sparse targets. Nevertheless, the solution of L1-norm constrained regularization method (L1-CRM) is sensitive to noise and the iterations would quickly diverge from the desired result if too many iterations are performed. In this paper, a regularization imaging method via joint L1-L2 norm constraint is proposed. By combing the L1 norm constraint and L2 norm constraint together, a new objective function is obtained and then the popular fast iterative threshold/shrinkage (FIST) method is adopted to minimize this nonsmooth convex function. This new method can not only have strong ability in resolving the sparse targets, but also be more robust to the noise. Simulations are carried out to demonstrate the effectiveness of the proposed method in terms of resolving ability and noise robustness.