Two-dimensional angle estimation of two-parallel nested arrays based on sparse bayesian estimation

Abstract

To increase the number of estimable signal sources, two-parallel nested arrays are proposed, which consist of two subarrays with M sensors, and can estimate the two-dimensional (2-D) direction of arrival (DOA) of M2 signal sources. To solve the problem of direction finding with two-parallel nested arrays, a 2-D DOA estimation algorithm based on sparse Bayesian estimation is proposed. Through a vectorization matrix, smoothing reconstruction matrix and singular value decomposition (SVD), the algorithm reduces the size of the sparse dictionary and data noise. A sparse Bayesian learning algorithm is used to estimate one dimension angle. By a joint covariance matrix, another dimension angle is estimated, and the estimated angles from two dimensions can be automatically paired. The simulation results show that the number of DOA signals that can be estimated by the proposed two-parallel nested arrays is much larger than the number of sensors. The proposed two-dimensional DOA estimation algorithm has excellent estimation performance.