Subspace perturbation bounds with an application to angle of arrival estimation using the MUSIC algorithm

Connor Delaosa; Jennifer Pestana; Stephan Weiss; Ian Proudler

doi:10.1109/SSPD47486.2020.9272125

Subspace perturbation bounds with an application to angle of arrival estimation using the MUSIC algorithm

Connor Delaosa, Jennifer Pestana, Stephan Weiss, Ian Proudler

Research output: Contribution to conference › Paper › peer-review

1 Citation (Scopus)

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Abstract

This paper explores how angle of arrival (AoA) estimation using the multiple signal classification (MUSIC) algorithm, is affected by estimation errors in space-time covariance matrix. In particular, we explore how this estimation error perturbs the signal-plus-noise and noise-only subspaces of this matrix, and how this subsequently affects the performance of MUSIC for AoA estimation. This subspace perturbation is shown to depend on the space-time covariance matrix itself, the sample size over which it is estimated, as well as the distance of the smallest signal-related eigenvalue to the noise floor. We link a bound of this perturbation to a bound on MUSIC performance, and demonstrate its utility for AoA estimation in simulations.

Original language	English
Pages	1-5
Number of pages	5
DOIs	https://doi.org/10.1109/SSPD47486.2020.9272125
Publication status	Published - 30 Nov 2020
Event	International Conference on Sensor Signal Processing for Defence - Edinburgh, United Kingdom Duration: 15 Sept 2020 → 16 Sept 2020 https://sspd.eng.ed.ac.uk/

Conference

Conference	International Conference on Sensor Signal Processing for Defence
Abbreviated title	SSPD
Country/Territory	United Kingdom
City	Edinburgh
Period	15/09/20 → 16/09/20
Internet address	https://sspd.eng.ed.ac.uk/

Keywords

space-time covariance matrix
parahermitian matrix
cross-correlation sequence
angle of arrival
multiple signal classification

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10.1109/SSPD47486.2020.9272125

Delaosa-etal-SSPD-2020-Subspace-perturbation-bounds-with-an-application-to-angleAccepted author manuscript, 401 KB

Cite this

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title = "Subspace perturbation bounds with an application to angle of arrival estimation using the MUSIC algorithm",

abstract = "This paper explores how angle of arrival (AoA) estimation using the multiple signal classification (MUSIC) algorithm, is affected by estimation errors in space-time covariance matrix. In particular, we explore how this estimation error perturbs the signal-plus-noise and noise-only subspaces of this matrix, and how this subsequently affects the performance of MUSIC for AoA estimation. This subspace perturbation is shown to depend on the space-time covariance matrix itself, the sample size over which it is estimated, as well as the distance of the smallest signal-related eigenvalue to the noise floor. We link a bound of this perturbation to a bound on MUSIC performance, and demonstrate its utility for AoA estimation in simulations.",

keywords = "space-time covariance matrix, parahermitian matrix, cross-correlation sequence, angle of arrival, multiple signal classification",

author = "Connor Delaosa and Jennifer Pestana and Stephan Weiss and Ian Proudler",

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doi = "10.1109/SSPD47486.2020.9272125",

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AU - Delaosa, Connor

AU - Pestana, Jennifer

AU - Weiss, Stephan

AU - Proudler, Ian

N1 - ©2020 IEEE C. Delaosa, J. Pestana, S. Weiss and I. K. Proudler, "Subspace Perturbation Bounds with an Application to Angle of Arrival Estimation using the MUSIC Algorithm," 2020 Sensor Signal Processing for Defence Conference (SSPD), Edinburgh, UK, 2020, pp. 1-5, doi: 10.1109/SSPD47486.2020.9272125.

PY - 2020/11/30

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N2 - This paper explores how angle of arrival (AoA) estimation using the multiple signal classification (MUSIC) algorithm, is affected by estimation errors in space-time covariance matrix. In particular, we explore how this estimation error perturbs the signal-plus-noise and noise-only subspaces of this matrix, and how this subsequently affects the performance of MUSIC for AoA estimation. This subspace perturbation is shown to depend on the space-time covariance matrix itself, the sample size over which it is estimated, as well as the distance of the smallest signal-related eigenvalue to the noise floor. We link a bound of this perturbation to a bound on MUSIC performance, and demonstrate its utility for AoA estimation in simulations.

AB - This paper explores how angle of arrival (AoA) estimation using the multiple signal classification (MUSIC) algorithm, is affected by estimation errors in space-time covariance matrix. In particular, we explore how this estimation error perturbs the signal-plus-noise and noise-only subspaces of this matrix, and how this subsequently affects the performance of MUSIC for AoA estimation. This subspace perturbation is shown to depend on the space-time covariance matrix itself, the sample size over which it is estimated, as well as the distance of the smallest signal-related eigenvalue to the noise floor. We link a bound of this perturbation to a bound on MUSIC performance, and demonstrate its utility for AoA estimation in simulations.

KW - space-time covariance matrix

KW - parahermitian matrix

KW - cross-correlation sequence

KW - angle of arrival

KW - multiple signal classification

UR - https://sspd.eng.ed.ac.uk/

U2 - 10.1109/SSPD47486.2020.9272125

DO - 10.1109/SSPD47486.2020.9272125

M3 - Paper

SP - 1

EP - 5

T2 - International Conference on Sensor Signal Processing for Defence

Y2 - 15 September 2020 through 16 September 2020

ER -

Subspace perturbation bounds with an application to angle of arrival estimation using the MUSIC algorithm

Abstract

Conference

Keywords

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Signal Processing in the Information Age (UDRC III)

Fundamentals, Techniques and Algorithms for Processing Multichannel Broadband Data | Delaosa, Connor

Fundamentals, Techniques and Algorithms for Processing Multichannel Broadband Data

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Subspace perturbation bounds with an application to angle of arrival estimation using the MUSIC algorithm

Abstract

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Projects

Signal Processing in the Information Age (UDRC III)

Fundamentals, Techniques and Algorithms for Processing Multichannel Broadband Data | Delaosa, Connor

Fundamentals, Techniques and Algorithms for Processing Multichannel Broadband Data

Cite this