Improved 2D Coprime Array Structure with the Difference and Sum Coarray Concept

Abstract

Recently, the difference and sum (diff-sum) coarray has attracted much attention in one-dimensional direction-of-arrival estimation for its high degrees-of-freedom (DOFs). In this paper, we utilize both the spatial information and the temporal information to construct the diff-sum coarray for planar sparse arrays. The diff-sum coarray contains both the difference coarray and the sum coarray, which provides much higher DOFs than the difference coarray alone. We take a planar coprime array consisting of two uniform square subarrays as the array model. To fully use the aperture-extending ability of the diff-sum coarray, we propose two novel configurations to improve the planar coprime array. The first configuration compresses the inter-element spacing of one subarray and results in a larger consecutive area in the coarray. The second configuration rearranges the two subarrays and introduces a proper separation between them, which can significantly reduce the redundancy of the diff-sum coarray and increase the DOFs. Besides, we derive the closed-form expressions of the central consecutive ranges in the coarrays of the proposed array configurations. Simulations verify the superiority of the proposed array configurations.

Sparse Billboard and T-Shaped Arrays for Two-Dimensional Direction of Arrival Estimation

“…In the literature, various 2D-DOA estimation algorithms were formulated based on the SODC [6], [13], [14], [15], [16], sum coarray, or a combination of both [2], [17]. Few references, however, have exploited higher-order difference coarrays (4th-order) [4], [18], [19], [20], [21].…”

Fast Underdetermined DOA Estimation Based on Generalized MRA via Original Covariance Vector Sparse Reconstruction

“…Lately, several new sparse array structures with closed form expression were proposed, such as the nested array (NA) [6] [7], coprime array (CPA) [8]- [10], generalized coprime array (GCPA) [11], super nested array (SNA) [12], generalized nested array (GNA) [13] and their combinations [14]. However, these sparse arrays all have less consecutive virtual sensors in the difference co-array [15]- [18] than the MRA under the same number of physical sensors while the number of consecutive virtual sensors is the main factor of underdetermined DOA estimation performance. Hence, we extend the definition of the MRA and propose a new array geometry of generalized minimum redundancy array (GMRA) with high degrees of freedom and low coupling effect.…”