Improved polynomial MUSIC algorithm for low-complexity and high-accuracy broadband AoA estimation

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Khattak, Faizan A. and Weiss, Stephan and Bakhit, Mohammed and Proudler, Ian K.; (2025) Improved polynomial MUSIC algorithm for low-complexity and high-accuracy broadband AoA estimation. In: 2025 IEEE Statistical Signal Processing Workshop (SSP). IEEE/SP Workshop on Statistical Signal Processing (SSP) . IEEE, GBR. (In Press)

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Abstract

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 polynomial eigenvalue decomposition (PEVD). However, PEVD is computationally intensive. In this paper, we propose a novel approach that bypasses the need for 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. Through simulations performed at 5db signal to noise ratio (SNR), we compare our method against the existing polynomial MUSIC algorithm that utilize sequential matrix diagonalization (SMD) PEVD technique. The results demonstrate that our approach offers superior accuracy and computational efficiency.

ORCID iDs

Khattak, Faizan A., Weiss, Stephan ORCID logoORCID: https://orcid.org/0000-0002-3486-7206, Bakhit, Mohammed and Proudler, Ian K.;
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