In this letter, the issue of two-dimensional (2-D) direction-of-arrival (DOA) estimation is discussed, and an extended virtual array based on covariance matrix (CM) or fourth-order cumulant (FOC) of signals from two subarrays is derived. Specifically, proposed method makes full use of sensors’ information by constructing virtual extended array, they can reduce the influence of noise and improve the Degrees-of-freedoms (DOFs) of arrays, thereby improving the estimation accuracy. Subsequently, the auto-paired angles can be obtained by CM of two subarrays (CMT). Simulations demonstrate the advantages of the proposed approach over the old one.

In this letter, an extended virtual array based on sum-difference co-array (SDC) of parallel sparse array (PSA) is derived, which have more Degrees-of-freedoms (DOFs) than the conventional virtual array only based on difference co-array (DC), for two-dimensional (2-D) direction of arrival (DOA) estimation of non-circular (NC) signal. Specifically, be placing two subarrays symmetrically with the coordinate axis, the com-plex joint three-dimensional (3-D) problem of 2-D DOA and one-dimensional (1-D) NC phase is simplified to two 1-D problems, which greatly reduces the computational complexity and improves the accuracy of results. At end, the effectiveness and superiority of the proposed algorithm are validated by numerical simulations.