DOA Estimation Based on Root Sparse Bayesian Learning Under Gain and Phase Error
Dingke Yu, Xin Wang, Wenwei Fang, Zixian Ma, Bing Lan, Chunyi Song, Zhiwei Xu
Abstract
The direction of arrival (DOA) is approximated by first-order Taylor expansion in most of the existing methods, which will lead to limited estimation accuracy when using coarse mesh owing to the off-grid error. In this paper, a new root sparse Bayesian learning based DOA estimation method robust to gain-phase error is proposed, which dynamically adjusts the grid angle under coarse grid spacing to compensate the off-grid error and applies the expectation maximization (EM) method to solve the respective iterative formula-based on the prior distribution of each parameter. Simulation results verify that the proposed method reduces the computational complexity through coarse grid sampling while maintaining a reasonable accuracy under gain and phase errors, as compared to the existing methods.