DOA Estimation of Coherent Signals Exploiting Forward/Backward Convolutional Kernels
Jun Zhao, Xudong Dong, Han Zhang, Meng Sun · IEEE Signal Processing Letters · 2025
The traditional subspace-based algorithms in the process of coherent direction of arrival (DOA) estimation get in trouble because of the rank loss of the signal covariance matrix. To this end, this paper introduces a forward/backward convolution kernel (FBCK) method, which not only reconstructs the signal covariance matrix and its diagonal elements, but also efficiently solves the signal coherence problem by utilizing the moving array technique. More precisely, the FBCK operation is applied to the signal space matrix at a given instant and utilizes the forward/backward convolution kernel to recover the rank corresponding to the number of signals without loss of the arrays' aperture. In a comparison evaluation with state-of-the-art spatial smoothing methods (including MSSP, SSP, ESS, ESS-SS, SSS and ASS), the proposed FBCK algorithm demonstrates excellent estimation capabilities in terms of snapshot number and signal-to-noise ratio (SNR), thus providing a robust and effective solution for DOA estimation in coherent signal environments.