Super-resolution Doppler beam sharpening imaging via sparse representation

In Doppler beam sharpening (DBS) imaging, the imaging scene is characterised corresponding to the Doppler band, and the Doppler band occupies only a small part compared with the whole frequency domain. Accordingly, the DBS image is sparse in the frequency domain. Motivated by the sparsity, the authors propose a novel framework of DBS formation via sparse representation to perform super-resolution. In the framework, by exploiting the fact that the ground scene is sparse in frequency domain, they perform the super-resolution formation by incorporating the sparsity constraint with respect to a redundant time–frequency dictionary. The recovered sparse coefficients are utilised to form the final DBS image in frequency domain. Since the dictionary is redundant with more columns than rows, a thinner Doppler frequency resolution and a higher sharpening ratio can be achieved. Experimental results on real measured data verify the effectiveness of the new super-resolution algorithm.