Direction of Arrival Estimation in Linear Arrays With Intersubarray Displacement Errors Using Sparse Bayesian Inference

Abstract

Linear arrays that are composed of multiple well-calibrated subarrays are widely used to improve the angular resolution in array signal processing. However, intersubarray displacement errors (IDEs) that are ubiquitous in practical applications and degrade the performance of the direction of arrival (DOA) estimation. To address this problem, we build a DOA estimation model that considers manifold mismatch due to the IDEs. The model is solved in the framework of sparse Bayesian inference with the variational inference methodology. The root-mean-square-errors of the DOA estimates of the proposed method are smaller in comparison with the sparsity-cognizant total least-squares approach. The improved method is applied to partially calibrate a virtual linear array that is constructed via the extended towed array measurement method in the case of velocity mismatch. We assume that the IDEs of a virtual linear array lead to a biased DOA estimate. Simulations and experimental results demonstrate the validity of the proposed method.