Azimuth Sampling Optimization Scheme for Sparse Microwave Imaging Based on Mutual Coherence Criterion

Sparse microwave imaging is a novel theory that systematically introduces sparse signal processing to microwave imaging. Compared with conventional synthetic aperture radar imaging, sparse microwave imaging exhibits the advantage of better imagery quality and lower system complexity. Non-ambiguity reconstruction for sparse scene can be achieved on under-sampling raw data by means of sparse microwave imaging, which leads to total data amount reduction. The imagery quality of sparse microwave imaging depends on the recovery property of measurement matrix, which is affected by the sparse sampling strategy. This paper focuses on the problem of design the azimuth sparse sampling scheme. The connection between mutual coherence and recovery property of the measurement matrix is analyzed. A mutual coherence based criterion is then proposed and applied to optimize the existing azimuth sparse sampling scheme. Numerical results demonstrate the effectiveness of the proposed method and conclusions are discussed.