Unfolded Coprime Planar Array for 2D Direction of Arrival Estimation: An Aperture-Augmented Perspective

Abstract

Generally, a coprime planar array (CPA) incorporates two uniform planar subarrays with large inter-element spacing to achieve improved direction of arrival (DOA) estimation performance, where the two subarrays are usually interleaved in the same quadrant. In this paper, by separating the two subarrays in different quadrants, we design an unfolded CPA to achieve the augmented array aperture. We study the theoretical performance of 2-D DOA estimation with unfolded CPA via Cramer-Rao bound, which concludes that the unfolded CPA with two subarrays in centrosymmetric layout can outperform those in other layouts in DOA estimation performance. Furthermore, we propose an ambiguity-free multiple signals classification (AF-MUSIC) algorithm by stacking the received signals of two subarrays, and prove that the ambiguity problem can be suppressed with coprime property. Meanwhile, AF-MUSIC algorithm can fully exploit all the degrees of freedom and attain more accurate DOA estimates due to the joint utilization of autocorrelation and cross-correlation information. In addition, a successive scheme for AF-MUSIC algorithm is devised to relieve the computational burden. Finally, numerical simulations verify the effectiveness and superiority of the unfolded CPA and AF-MUSIC algorithm.