基于相关准则的稀疏微波成像方位向采样优化方法

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

稀疏微波成像将稀疏信号处理理论系统性地引入微波成像中,与传统合成孔径雷达成像相比,具有提高成像质量、降低系统复杂度等优点。稀疏采样方式是影响稀疏微波成像重建质量的重要因素。该文主要研究方位向稀疏采样的优化问题,分析了稀疏微波成像观测矩阵的相关系数与重建能力的关系,在此基础上提出一种基于相关系数的优化准则,并对方位向稀疏采样参数进行优化。仿真结果验证了所提优化方法的有效性。

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.