Two-Dimensional DOA Estimation by Using Two Parallel Coprime-Nested Arrays

In this paper, we address the problem of two-dimensional (2-D) direction-of-arrival (DOA) estimation for two parallel coprime-nested arrays (TPCNAs), which can be divided into two parallel sparse nested arrays (TPSNAs). By vectorizing the covariance and cross-covariance matrices and Toeplitz matrix reconstruction, a two parallel sparse uniform linear array with longer aperture is formed for each TPSNA. Subsequently, the root multiple signals classification is applied to each virtual array to reduce the complexity. According to the coprime property, the final estimates can be uniquely determined by the two virtual arrays. Simulations results are also provided to verify the performance of the proposed method.