MIMO Radar Super-Resolution Imaging Based on Reconstruction of the Measurement Matrix of Compressed Sensing
Jieru Ding, Min Wang, Hailong Kang, Zhiyi Wang
Abstract
This letter proposes a novel sparse recovery method of multiple-input and multiple-output (MIMO) radar compressed sensing (CS) imaging algorithms. This method leverages the prior structure of the measurement matrix to judge targets’ locations and to estimate the sparsity level in the grid roughly and finally inhabits the emergence of false targets in the imaging figure. Explicitly, the algorithm we propose is inspired by the orthogonal matching pursuit (OMP) algorithm. First, the measurement matrix can be divided into some submatrices by column. Then, we estimate which submatrices do not contain signal components by the algorithms we propose in this literature to achieve the reconstruction of the measurement matrix. Finally, we use the sparse Bayesian learning algorithm and the sparsity adaptive matching pursuit algorithm to recover the target location and scattering intensity. Experiments validate that the reconstruction error of the algorithm we propose is much lower than other sparse recovery algorithms, and targets in the imaging are more obvious than other algorithms.