TY - GEN
T1 - Robust Adaptive Beamforming Based on Interference Covariance Matrix Reconstruction and Steering Vector Estimation
AU - Wang, Ronggui
AU - Wang, Yuexian
AU - Han, Chuang
AU - Gong, Yanyun
AU - Wang, Ling
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/8/17
Y1 - 2021/8/17
N2 - The adaptive beamformer is very sensitive to model mismatches. In order to improve the robustness of the adaptive beamformer against mismatch errors, this paper proposes a novel robust adaptive beamforming (RAB) method. Firstly, we use a subspace-based method to estimate the steering vectors of signal-of-interest (SOI) and interference signals. The essence of this method is that each steering vector (SV) can be derived from the vector located at the intersection of two subspaces. Meanwhile, the estimate of each SV is given in a closed-form expression. Secondly, a simplified power estimation method that is independent of the array structure is used to estimate the interference and noise powers. Then based on the estimated SVs of interference and the powers of interference and noise, a more accurate interference-plus-noise covariance matrix (INCM) is reconstructed according to its definition. Finally, the proposed beamformer is obtained based on the more accurate SOI SV and INCM, which has higher robustness to array model mismatches. Simulation results demonstrate the effectiveness of the proposed RAB method.
AB - The adaptive beamformer is very sensitive to model mismatches. In order to improve the robustness of the adaptive beamformer against mismatch errors, this paper proposes a novel robust adaptive beamforming (RAB) method. Firstly, we use a subspace-based method to estimate the steering vectors of signal-of-interest (SOI) and interference signals. The essence of this method is that each steering vector (SV) can be derived from the vector located at the intersection of two subspaces. Meanwhile, the estimate of each SV is given in a closed-form expression. Secondly, a simplified power estimation method that is independent of the array structure is used to estimate the interference and noise powers. Then based on the estimated SVs of interference and the powers of interference and noise, a more accurate interference-plus-noise covariance matrix (INCM) is reconstructed according to its definition. Finally, the proposed beamformer is obtained based on the more accurate SOI SV and INCM, which has higher robustness to array model mismatches. Simulation results demonstrate the effectiveness of the proposed RAB method.
KW - Robust adaptive beamforming (RAB)
KW - covariance matrix reconstruction
KW - simplified power estimation
KW - steering vector (SV) estimation
UR - http://www.scopus.com/inward/record.url?scp=85118422121&partnerID=8YFLogxK
U2 - 10.1109/ICSPCC52875.2021.9564485
DO - 10.1109/ICSPCC52875.2021.9564485
M3 - 会议稿件
AN - SCOPUS:85118422121
T3 - Proceedings of 2021 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2021
BT - Proceedings of 2021 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2021
Y2 - 17 August 2021 through 19 August 2021
ER -