Improved polynomial MUSIC algorithm for low-complexity and high-accuracy broadband angle of arrival estimation

Tools

Khattak, Faizan A. and Bakhit, Mohammed and Proudler, Ian K. and Weiss, Stephan; (2025) Improved polynomial MUSIC algorithm for low-complexity and high-accuracy broadband angle of arrival estimation. In: 2025 IEEE Statistical Signal Processing Workshop (SSP). IEEE/SP Workshop on Statistical Signal Processing (SSP) . IEEE, GBR, pp. 261-265. ISBN 9798331518004 (https://doi.org/10.1109/SSP64130.2025.11073228)

[thumbnail of Khattak-etal-IEEE-SSP-2025-Improved-polynomial-MUSIC-algorithm]
Preview
 Text. Filename: Khattak-etal-IEEE-SSP-2025-Improved-polynomial-MUSIC-algorithm.pdf
Accepted Author Manuscript
License: Creative Commons Attribution 4.0 logo
Download (774kB)| Preview

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 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.

ORCID iDs

Khattak, Faizan A., Bakhit, Mohammed ORCID logoORCID: https://orcid.org/0000-0002-2951-0325, Proudler, Ian K. and Weiss, Stephan ORCID logoORCID: https://orcid.org/0000-0002-3486-7206;
CORE (COnnecting REpositories)