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

Faizan A. Khattak; Stephan Weiss; Mohammed Bakhit; Ian K. Proudler

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

Faizan A. Khattak, Stephan Weiss, Mohammed Bakhit, Ian K. Proudler

Electronic And Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

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.

Original language	English
Title of host publication	2025 IEEE Statistical Signal Processing Workshop (SSP)
Place of Publication	Piscataway, NJ
Publisher	IEEE
Number of pages	5
Publication status	Accepted/In press - 3 Apr 2025
Event	23rd IEEE Statistical Signal Processing Workshop - Edinburgh, United Kingdom Duration: 8 Jun 2025 → 11 Jun 2025 https://2025.ieeessp.org/

Publication series

Name	IEEE/SP Workshop on Statistical Signal Processing (SSP)
ISSN (Print)	2373-0803
ISSN (Electronic)	2693-3551

Conference

Conference	23rd IEEE Statistical Signal Processing Workshop
Abbreviated title	SSP 2025
Country/Territory	United Kingdom
City	Edinburgh
Period	8/06/25 → 11/06/25
Internet address	https://2025.ieeessp.org/

Keywords

Multiple Signal Classification (MUSIC)
angle-of-arrival
broadband
polynomial eigenvalue decomposition

Cite this

@inproceedings{ba33e13975414519a381c6f9c8c671f4,

title = "Improved polynomial MUSIC algorithm for low-complexity and high-accuracy broadband AoA estimation",

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.",

keywords = "Multiple Signal Classification (MUSIC), angle-of-arrival, broadband, polynomial eigenvalue decomposition",

author = "Khattak, {Faizan A.} and Stephan Weiss and Mohammed Bakhit and Proudler, {Ian K.}",

year = "2025",

month = apr,

day = "3",

language = "English",

series = "IEEE/SP Workshop on Statistical Signal Processing (SSP)",

publisher = "IEEE",

booktitle = "2025 IEEE Statistical Signal Processing Workshop (SSP)",

note = "23rd IEEE Statistical Signal Processing Workshop, SSP 2025 ; Conference date: 08-06-2025 Through 11-06-2025",

url = "https://2025.ieeessp.org/",

}

Khattak, FA, Weiss, S , Bakhit, M & Proudler, IK 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, Piscataway, NJ, 23rd IEEE Statistical Signal Processing Workshop, Edinburgh, United Kingdom, 8/06/25.

Improved polynomial MUSIC algorithm for low-complexity and high-accuracy broadband AoA estimation. / Khattak, Faizan A.; Weiss, Stephan ; Bakhit, Mohammed et al.
2025 IEEE Statistical Signal Processing Workshop (SSP). Piscataway, NJ: IEEE, 2025. (IEEE/SP Workshop on Statistical Signal Processing (SSP)).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

TY - GEN

T1 - Improved polynomial MUSIC algorithm for low-complexity and high-accuracy broadband AoA estimation

AU - Khattak, Faizan A.

AU - Weiss, Stephan

AU - Bakhit, Mohammed

AU - Proudler, Ian K.

PY - 2025/4/3

Y1 - 2025/4/3

N2 - 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.

AB - 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.

KW - Multiple Signal Classification (MUSIC)

KW - angle-of-arrival

KW - broadband

KW - polynomial eigenvalue decomposition

UR - https://2025.ieeessp.org/

UR - https://ieeexplore.ieee.org/xpl/conhome/10207925/proceeding

M3 - Conference contribution book

T3 - IEEE/SP Workshop on Statistical Signal Processing (SSP)

BT - 2025 IEEE Statistical Signal Processing Workshop (SSP)

PB - IEEE

CY - Piscataway, NJ

T2 - 23rd IEEE Statistical Signal Processing Workshop

Y2 - 8 June 2025 through 11 June 2025

ER -

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

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Keywords

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