2-D DOA Estimation Based on Rectangular Generalized Minimum Redundancy Array via Partial Grid Covariance Vector Sparse Reconstruction

In this paper, a novel rectangular sparse array with hole-free difference co-arrays and low coupling effect is proposed, while a sparse reconstruction algorithm of partial grid is also proposed for 2-D DOA estimation. Firstly, the nested Toeplitz characteristic of covariance matrix of rectangular uniform array is analysed and the nested Toeplitz covariance matrix is estimated. Based on the precisely estimated covariance matrix, we establish the sparse representation model of covariance vector and achieve 2-D DOA estimation by proposed partial grid covariance vector sparse reconstruction (PGCVSR). Simulation results demonstrate that our proposed algorithm can achieve superior 2-D DOA estimation performance and high estimation accuracy.