Underwater DOA estimation based on cross-correlation domain for relocating improved nested array

In the field of array signal processing research, there has been considerable attention to sparse arrays for the ability to provide larger array apertures and more degrees of freedom (DOFs) under the constraint of fixing the number of physical sensors to save the use of resources. Aiming at this, a novel relocating improved nested array (RINA) configuration with increasing DOFs and hole-free co-array is proposed and simple closed-form expressions for its array geometry and DOFs are given. Compared with most existing sparse array configurations, the proposed RINA can achieve more consecutive DOFs and larger effective array apertures. Based on the mentioned good properties of the proposed RINA, a cross-correlation domain (C-CD) algorithm is proposed for direction of arrival (DOA) estimation. The algorithm is based on the virtual co-array of RINA to build the cross-correlation domain, followed by the generalized eigenvalue method and the least squares method for noise compensation and optimization, respectively. The results of theoretical analysis and simulations demonstrate the superior performance of the proposed RINA. The simulation results show that the proposed C-CD algorithm has better DOA estimation performance compared with those of existing algorithms, and the advantage of its spatial spectrum without pseudo peaks can achieve fast estimation for source directions. The experimental results demonstrate the feasibility of the proposed RINA and C-CD algorithm applied to underwater DOA estimation.(c) 2022 Elsevier Inc. All rights reserved.