Compressive Sensing-Based Angle Estimation for MIMO Radar with Multiple Snapshots

Abstract

The issue of angle estimation for multiple-input multiple-output (MIMO) radar is studied and an algorithm for the estimation based on compressive sensing with multiple snapshots is proposed. The dimension of received signal is reduced to make the computation burden lower, and then the noise sensitivity is reduced by the eigenvalue decomposition (EVD) of the covariance matrix of the reduced-dimensional signal. Finally the signal subspace obtained from the eigenvectors is realigned to apply the orthogonal matching pursuit (OMP) for angle estimation. The angle estimation performance of the proposed algorithm is better than that of estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm, and reduced-dimension Capon. Furthermore, the proposed algorithm works well for coherent targets, and requires no knowledge of the noise. The complexity analysis and simulation results verify the effectiveness of the algorithm.