Adaptive Beamforming With Sensor Position Errors Using Covariance Matrix Construction Based on Subspace Bases Transition
Peng Chen, Yixin Yang, Yong Wang, Yuanliang Ma · IEEE Signal Processing Letters · 2018
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 beamformer outperforms other tested beamformers in the presence of sensor position errors.