Compressed Sensing-Based Angle Estimation for Noncircular Sources in MIMO Radar

Abstract

In this paper, we consider the problem of applying compressed sensing (CS) theory to angle estimation for noncircular sources in monostatic multiple-input multiple output (MIMO) radar, and propose an angle estimation algorithm based on extended matrix compressed sensing. Firstly, a reduced-dimensional matrix is employed to transform the data matrix into a low dimensional one. Then the properties of noncircular signals are utilized to construct an extended matrix from the received data. Finally, the dictionary can be conducted to apply 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 ESPRIT (RD-ESPRIT) algorithm, and the proposed method requires no knowledge of the noise. The simulation results verify the effectiveness of the algorithm.