Abstract
Currently many deconvolution methods, such as truncated singular value decomposition (TSVD) method and Tikhonov regularization method, have been applied to angle super-resolution imaging of airborne scanning radars. However in the imaging of remote and large-scale scenes, when the size of the convolution matrix becomes large, the amount of calculation rises exponentially with the increasement of convolution matrix. In this paper, the random singular value decomposition (RSVD) method is adopted to reduce the computational complexity from O(n3) to O(2nq2), q ≪ n. Simulation and experiment data result prove the proposed method can improve computational efficiency while holding performance.
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