A weighted ℓ2, 1 minimization with Pisarenko-class cepstrum for stepped frequency radar imaging

We propose a weighted ℓ_2,1 minimization algorithm for retrieving high resolution range profile with the stepped frequency radar. Based on the sliding window technique the raw stepped frequency pulse train is split into subsequences to form the jointly sparse signature. By using the Hadamard product of two Pisarenko framework cepstrums a support-related weighting scheme is designed to control the priority class of bases in the reconstruction process so that those bases whose indices are inside the support are likely selected. Experiments on simulated and real data demonstrate that the proposed algorithm performs better range profile reconstruction in comparison with existing homogeneous algorithms.