Fast Total Variation Method Based on Iterative Reweighted Norm for Airborne Scanning Radar Super-Resolution Imaging

The total variation (TV) method has been applied to realizing airborne scanning radar super-resolution imaging while maintaining the outline of the target. The iterative reweighted norm (IRN) approach is an algorithm for addressing the minimum Lp norm problem by solving a sequence of minimum weighted L2 norm problems, and has been applied to solving the TV norm. However, during the solving process, the IRN method is required to update the weight term and result term in each iteration, involving multiplications and the inversion of large matrices. Consequently, it suffers from a huge calculation load, which seriously restricts the application of the TV imaging method. In this work, by analyzing the structural characteristics of the matrix involved in iteration, an efficient method based on suitable matrix blocking is proposed. It transforms multiplications and the inversion of large matrices into the computation of multiple small matrices, thereby accelerating the algorithm. The proposed method, called IRN-FTV method, is more time economical than the IRN-TV method, especially for high dimensional observation scenarios. Numerical results illustrate that the proposed IRN-FTV method enjoys preferable computational efficiency without performance degradation.