A sparse direction-of-arrival estimation algorithm for MIMO radar in the presence of gain-phase errors

• Published in: Digital signal processing Vol. 69; pp. 193 - 203
• Main Authors: Liu, Jing, Zhou, Weidong, Wang, Xianpeng, Huang, Defeng (David)
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.10.2017
• Subjects: Direction-of-arrival estimation; Fourth-order cumulants; Gain-phase errors; Multiple-input multiple-output radar; Sparse representation; Sparse representation; Direction-of-arrival estimation; Fourth-order cumulants; Multiple-input multiple-output radar; Gain-phase errors
• ISSN: 1051-2004; 1095-4333
• DOI: 10.1016/j.dsp.2017.06.025

In this paper, the problem of direction-of-arrival (DOA) estimation for monostatic multiple-input multiple-output (MIMO) radar with gain-phase errors is addressed, by using a sparse DOA estimation algorithm with fourth-order cumulants (FOC) based error matrix estimation. Useful cumulants are designed and extracted to estimate the gain and the phase errors in the transmit array and the receive array, thus a reliable error matrix is obtained. Then the proposed algorithm reduces the gain-phase error matrix to a low dimensional one. Finally, with the updated gain-phase error matrix, the FOC-based reweighted sparse representation framework is introduced to achieve accurate DOA estimation. Thanks to the fourth-order cumulants based gain-phase error matrix estimation, and the reweighted sparse representation framework, the proposed algorithm performs well for both white and colored Gaussian noises, and provides higher angular resolution and better angle estimation performance than reduced-dimension MUSIC (RD-MUSIC), adaptive sparse representation (adaptive-SR) and ESPRIT-based algorithms. Simulation results verify the effectiveness and advantages of the proposed method. •DOA estimation problem for MIMO radar with gain-phase errors is considered.•Fourth-order cumulants are designed to achieve reliable error matrix estimation.•The proposed algorithm constructs the updated error matrix with a low dimension.•It outperforms the conventional methods for both white and colored noises.