Joint estimation of DOA and mutual coupling via block sparse Bayesian learning

The direction-of-arrival (DOA) estimation accuracy is sensitive to the unknown mutual coupling in antenna array. This paper proposes an online array calibration method which jointly estimates the DOAs and the mutual coupling. Taking advantage of the Toeplitz property of mutual coupling matrix (MCM) with the uniform linear array (ULA), we handle the joint estimation problem by exploiting the block sparse Bayesian learning (BSBL) framework. This method does not need the calibrating sources or the preliminary MCM. Due to the robustness of the BSBL, this method is also effective under coherent scenario. The performance of the proposed method is compared with other well-known calibration algorithms and the Cramer-Rao lower bound (CRLB). The results show that it achieves higher accuracy and resolution.