Off-Grid DOA Estimation of Coherent Signals Using Weighted Sparse Bayesian Inference

In this paper, a high-precision direction-of-arrival (DOA) estimation of coherent signals algorithm using the improved off-grid sparse Bayesian inference is proposed. Firstly, we construct a weighted vector via IMUSIC algorithm to provide a priori information of spatial distribution, which can improve the estimation accuracy and efficiency. Then, to further reduce the approximate error of the first-order off-grid model, the steering vector is reformulated by the second-order Taylor expansion. Finally, the steering vector higherorder approximation model and weighted sparse Bayesian inference are combined together to realize the estimation of DOA. Extensive simulation results demonstrate the superior performance of the proposed algorithm under the conditions of coherent signals and low SNR.