TY - JOUR
T1 - A sparse direction-of-arrival estimation algorithm for MIMO radar in the presence of gain-phase errors
AU - Liu, Jing
AU - Zhou, Weidong
AU - Wang, Xianpeng
AU - Huang, Defeng David
PY - 2017/10/1
Y1 - 2017/10/1
N2 - 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. © 2017 Elsevier Inc.
AB - 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. © 2017 Elsevier Inc.
KW - Direction-of-arrival estimation
KW - Fourth-order cumulants
KW - Gain-phase errors
KW - Multiple-input multiple-output radar
KW - Sparse representation
UR - http://www.scopus.com/inward/record.url?scp=85023623672&partnerID=8YFLogxK
U2 - 10.1016/j.dsp.2017.06.025
DO - 10.1016/j.dsp.2017.06.025
M3 - Article
AN - SCOPUS:85023623672
SN - 1051-2004
VL - 69
SP - 193
EP - 203
JO - Digital Signal Processing
JF - Digital Signal Processing
ER -