Two-Dimensional DOA Estimation via a Novel Sparse Array Consisting of Coprime and Nested Subarrays

Abstract

In this letter, a novel sparse array consisting of coprime and nested subarrays is designed and a corresponding two-dimensional (2-D) direction of arrival (DOA) estimation method is presented. The proposed array can be decomposed into two sparse planar subarrays, each of which consists of some identical nested linear arrays (NLAs). Especially, the dense subarrays in these NLAs can form the prototype coprime arrays. Then, by vectorizing two covariance matrices, two virtual coprime planar subarrays are available, which have much larger apertures than the physical ones. Subsequently, 2-D spatial smoothing and multiple signal classification are utilized to get two coprime estimations. Combining them, the unique DOA estimation is determined. Theoretical analysis and numerical simulations demonstrate the effectiveness and superiority of the proposed method.