High-resolution distributed array DOA estimation based on phase offset recovery between subarrays

Abstract

In widely existing dynamic scenes, position errors exist between subarrays of the distributed array, and the phase offset between subarrays caused by position errors seriously affects the method performance. However, on the one hand, the existing direction of arrival (DOA) estimation methods in distributed array have low phase offset recovery accuracy between subarrays with fewer snapshot numbers and smaller target azimuthal spacing, which in turn fails to effectively improve the DOA estimation accuracy. On the other hand, the spectral peak search and gradient descent are limited by parameter settings such as step size, initial value, or scan interval, which leads to low accuracy of azimuthal estimation. Consequently, in this work, a novel phase offset recovery method between subarrays is proposed. The proposed method constructs an objective function according to the cost function of the blind source separation (BSS) method and designs a quantum coronavirus herd immunity optimizer (QCHIO) to solve this objective function, which achieves the phase offset recovery. Then another objective function is constructed according to the recovered phase offset and the nonlinear least squares (NLS) idea. And this function is solved through QCHIO, which improves the accuracy of the distributed array direction finding method. Finally, numerical simulations demonstrate that the proposed method has higher phase offset recovery and DOA estimation accuracy compared to the comparison methods with fewer snapshot numbers and smaller target azimuthal spacing, and it does not require parameter settings such as the initial value or searching step size.