Enhanced space-time covariance estimation based on a system identification approach

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

doi:10.1109/SSPD54131.2022.9896183

Enhanced space-time covariance estimation based on a system identification approach

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

Electronic And Electrical Engineering

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

2 Citations (Scopus)

41 Downloads (Pure)

Abstract

The error inflicted on a space-time covariance estimate due to the availability of only finite data is known to perturb the eigenvalues and eigenspaces of its z-domain equivalent, i.e., the cross-spectral density matrix. In this paper, we show that a significantly more accurate estimate can be obtained if the source signals driving the signal model are also accessible, such that a system identication approach for the source model becomes viable. We demonstrate this improved accuracy in simulations, and discuss its dependencies on the sample size and the signal to noise ratio of the data.

Original language	English
Title of host publication	2022 Sensor Signal Processing for Defence Conference, SSPD 2022 - Proceedings
Place of Publication	Piscataway, NJ.
Publisher	IEEE
Number of pages	5
ISBN (Electronic)	9781665483483
ISBN (Print)	9781665483483
DOIs	https://doi.org/10.1109/SSPD54131.2022.9896183
Publication status	Published - 23 Sept 2022
Event	11th International Conference in Sensor Signal Processing for Defence: from Sensor to Decision - London, United Kingdom Duration: 13 Sept 2022 → 14 Sept 2022 Conference number: 11th https://sspd.eng.ed.ac.uk/

Publication series

Name	2022 Sensor Signal Processing for Defence Conference, SSPD 2022 - Proceedings

Conference

Conference	11th International Conference in Sensor Signal Processing for Defence
Abbreviated title	SSPD 2022
Country/Territory	United Kingdom
City	London
Period	13/09/22 → 14/09/22
Internet address	https://sspd.eng.ed.ac.uk/

Keywords

enhanced space-time covariance
estimation
system identification approach
eigenvalues
eigenspaces
improved accuracy

Access to Document

10.1109/SSPD54131.2022.9896183Licence: Unspecified

Khattak-etal-SSPD-2022-Enhanced-space-time-covariance-estimation-based-on-a-system-identification-approachAccepted author manuscript, 707 KBLicence: Strath_1

Cite this

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title = "Enhanced space-time covariance estimation based on a system identification approach",

abstract = "The error inflicted on a space-time covariance estimate due to the availability of only finite data is known to perturb the eigenvalues and eigenspaces of its z-domain equivalent, i.e., the cross-spectral density matrix. In this paper, we show that a significantly more accurate estimate can be obtained if the source signals driving the signal model are also accessible, such that a system identication approach for the source model becomes viable. We demonstrate this improved accuracy in simulations, and discuss its dependencies on the sample size and the signal to noise ratio of the data.",

keywords = "enhanced space-time covariance, estimation, system identification approach, eigenvalues, eigenspaces, improved accuracy",

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

note = "{\textcopyright} 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.; 11th International Conference in Sensor Signal Processing for Defence : from Sensor to Decision, SSPD 2022 ; Conference date: 13-09-2022 Through 14-09-2022",

year = "2022",

month = sep,

day = "23",

doi = "10.1109/SSPD54131.2022.9896183",

language = "English",

isbn = "9781665483483",

series = "2022 Sensor Signal Processing for Defence Conference, SSPD 2022 - Proceedings",

publisher = "IEEE",

booktitle = "2022 Sensor Signal Processing for Defence Conference, SSPD 2022 - Proceedings",

url = "https://sspd.eng.ed.ac.uk/",

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Khattak, FA , Proudler, IK & Weiss, S 2022, Enhanced space-time covariance estimation based on a system identification approach. in 2022 Sensor Signal Processing for Defence Conference, SSPD 2022 - Proceedings. 2022 Sensor Signal Processing for Defence Conference, SSPD 2022 - Proceedings, IEEE, Piscataway, NJ., 11th International Conference in Sensor Signal Processing for Defence, London, United Kingdom, 13/09/22. https://doi.org/10.1109/SSPD54131.2022.9896183

Enhanced space-time covariance estimation based on a system identification approach. / Khattak, Faizan A.; Proudler, Ian K.; Weiss, Stephan.
2022 Sensor Signal Processing for Defence Conference, SSPD 2022 - Proceedings. Piscataway, NJ.: IEEE, 2022. (2022 Sensor Signal Processing for Defence Conference, SSPD 2022 - Proceedings).

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

TY - GEN

T1 - Enhanced space-time covariance estimation based on a system identification approach

AU - Khattak, Faizan A.

AU - Proudler, Ian K.

AU - Weiss, Stephan

N1 - Conference code: 11th

PY - 2022/9/23

Y1 - 2022/9/23

N2 - The error inflicted on a space-time covariance estimate due to the availability of only finite data is known to perturb the eigenvalues and eigenspaces of its z-domain equivalent, i.e., the cross-spectral density matrix. In this paper, we show that a significantly more accurate estimate can be obtained if the source signals driving the signal model are also accessible, such that a system identication approach for the source model becomes viable. We demonstrate this improved accuracy in simulations, and discuss its dependencies on the sample size and the signal to noise ratio of the data.

AB - The error inflicted on a space-time covariance estimate due to the availability of only finite data is known to perturb the eigenvalues and eigenspaces of its z-domain equivalent, i.e., the cross-spectral density matrix. In this paper, we show that a significantly more accurate estimate can be obtained if the source signals driving the signal model are also accessible, such that a system identication approach for the source model becomes viable. We demonstrate this improved accuracy in simulations, and discuss its dependencies on the sample size and the signal to noise ratio of the data.

KW - enhanced space-time covariance

KW - estimation

KW - system identification approach

KW - eigenvalues

KW - eigenspaces

KW - improved accuracy

UR - https://ieeexplore.ieee.org/xpl/conhome/1804224/all-proceedings

U2 - 10.1109/SSPD54131.2022.9896183

DO - 10.1109/SSPD54131.2022.9896183

M3 - Conference contribution book

SN - 9781665483483

T3 - 2022 Sensor Signal Processing for Defence Conference, SSPD 2022 - Proceedings

BT - 2022 Sensor Signal Processing for Defence Conference, SSPD 2022 - Proceedings

PB - IEEE

CY - Piscataway, NJ.

T2 - 11th International Conference in Sensor Signal Processing for Defence

Y2 - 13 September 2022 through 14 September 2022

ER -

Enhanced space-time covariance estimation based on a system identification approach

Abstract

Publication series

Conference

Keywords

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Signal Processing in the Information Age (UDRC III)

Cite this

Enhanced space-time covariance estimation based on a system identification approach

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Projects

Signal Processing in the Information Age (UDRC III)

Cite this