Improved Polynomial MUSIC Algorithm for Low-Complexity and High-Accuracy Broadband Angle of Arrival Estimation

The Multiple Signal Classification (MUSIC) algorithm has been extended to broadband angle-of-arrival (AoA) estimation through the development of polynomial MUSIC, which relies on a polynomial eigenvalue decomposition (PEVD). However, a PEVD is computationally intensive. In this paper, we propose a novel approach that bypasses the need for a PEVD by directly computing the polynomial subspace projection matrix corresponding to the noise subspace by computing EVD within the discrete Fourier transform (DFT) bins of a space-time covariance. In simulations, we demonstrate that our approach can offer superior accuracy and computational efficiency compared to the existing polynomial MUSIC algorithm.