Adaptive Beamforming With Sensor Position Errors Using Covariance Matrix Construction Based on Subspace Bases Transition

作者：Chen, Peng; Yang, Yixin^*; Wang, Yong; Ma, Yuanliang

来源：IEEE Signal Processing Letters, 2019, 26(1): 19-23.

DOI：10.1109/LSP.2018.2878948

• Bases transition
• covariance matrix construction
• robust adaptive beamforming
• sensor position error

This letter proposes a narrowband interference-plus-noise covariance matrix (INCM) based beamformer, which is robust with sensor position errors for linear array. First, using the subspace fitting and subspace orthogonality techniques, we estimate a set of angle-related bases for the signal-plus-interference subspace (SIS) by solving a joint optimization problem. Second, we obtain the bases transition matrix between the estimated angle-related bases and the orthogonal bases consisting of the dominant eigenvectors of the sample covariance matrix (SCM). The SCM can be expressed as a function of the angle-related bases and the bases transition matrix. We construct the INCM directly from the SIS by eliminating the component of the desired signal from the angle-related bases. Simulations and experimental results show that the proposed heamformer outperforms other tested heamformers in the presence of sensor position errors.

• 出版日期2019-1
• 单位西北工业大学