High-Resolution Radar Imaging of Air Targets From Sparse Azimuth Data

This paper derives the signal model for radar imaging of air targets from sparse azimuth data. Then, the sparsity of two data-missing patterns, i.e., the gapped data and random missing data, is studied following the theory of sparse signal representation. The missing samples are grouped together with continuous data segments in the former pattern, while they are placed randomly with a uniform distribution in the latter one. After that, a practical procedure for imaging from sparse azimuth data is proposed. In this procedure, a new method is introduced for gapped-data range alignment. Then, different imaging methods are chosen according to the mutual coherence of the overcomplete basis. For a small mutual coherence, the imaging method founded on basis pursuit (BP) is proposed. For the gapped data with a large mutual coherence, the gapped-data amplitude and phase estimation (GAPES) is applied to azimuth imaging. Finally, imaging results of measured sparse azimuth data have proved the effectiveness of the proposed method.