Sample space-time covariance matrix estimation

Connor Delaosa; Jennifer Pestana; Nicholas J. Goddard; Samuel Somasundaram; Stephan Weiss

Sample space-time covariance matrix estimation

Connor Delaosa, Jennifer Pestana, Nicholas J. Goddard, Samuel Somasundaram, Stephan Weiss

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

15 Citations (Scopus)

93 Downloads (Pure)

Abstract

Estimation errors are incurred when calculating the sample space-time covariance matrix. We formulate the variance of this estimator when operating on a finite sample set, compare it to known results, and demonstrate its precision in simulations. The variance of the estimation links directly to previously explored perturbation of the analytic eigenvalues and eigenspaces of a parahermitian cross-spectral density matrix when estimated from finite data.

Original language	English
Pages	8033-8037
Number of pages	5
Publication status	Published - 16 May 2019
Event	2019 International Conference on Acoustics, Speech, and Signal Processing - Brighton, United Kingdom Duration: 12 May 2019 → 17 May 2019

Conference

Conference	2019 International Conference on Acoustics, Speech, and Signal Processing
Abbreviated title	ICASSP 2019
Country/Territory	United Kingdom
City	Brighton
Period	12/05/19 → 17/05/19

Keywords

space-time covariance
estimation
parahermitian matrix EVD
polynomial matrices

Access to Document

Delaosa-etal-ICASSP-2019-Sample-space-time-covariance-matrix-estimationAccepted author manuscript, 484 KB

1 Finished

Signal Processing in the Information Age (UDRC III)
Weiss, S. (Principal Investigator) & Stankovic, V. (Co-investigator)
EPSRC (Engineering and Physical Sciences Research Council)
1/07/18 → 31/03/24
Project: Research

15 Citations
1 Paper

Space-time covariance matrix estimation: loss of algebraic multiplicities of Eigenvalues
Khattak, F. A., Weiss, S., Proudler, I. K. & McWhirter, J. G., 3 Nov 2022, p. 975-979. 5 p.
Research output: Contribution to conference › Paper › peer-review

Open Access
File

Cite this

@conference{016e95fe6c514110a017de007e06052e,

title = "Sample space-time covariance matrix estimation",

abstract = "Estimation errors are incurred when calculating the sample space-time covariance matrix. We formulate the variance of this estimator when operating on a finite sample set, compare it to known results, and demonstrate its precision in simulations. The variance of the estimation links directly to previously explored perturbation of the analytic eigenvalues and eigenspaces of a parahermitian cross-spectral density matrix when estimated from finite data.",

keywords = "space-time covariance, estimation, parahermitian matrix EVD, polynomial matrices",

author = "Connor Delaosa and Jennifer Pestana and Goddard, {Nicholas J.} and Samuel Somasundaram and Stephan Weiss",

year = "2019",

month = may,

day = "16",

language = "English",

pages = "8033--8037",

note = "2019 International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 ; Conference date: 12-05-2019 Through 17-05-2019",

}

TY - CONF

T1 - Sample space-time covariance matrix estimation

AU - Delaosa, Connor

AU - Pestana, Jennifer

AU - Goddard, Nicholas J.

AU - Somasundaram, Samuel

AU - Weiss, Stephan

PY - 2019/5/16

Y1 - 2019/5/16

N2 - Estimation errors are incurred when calculating the sample space-time covariance matrix. We formulate the variance of this estimator when operating on a finite sample set, compare it to known results, and demonstrate its precision in simulations. The variance of the estimation links directly to previously explored perturbation of the analytic eigenvalues and eigenspaces of a parahermitian cross-spectral density matrix when estimated from finite data.

AB - Estimation errors are incurred when calculating the sample space-time covariance matrix. We formulate the variance of this estimator when operating on a finite sample set, compare it to known results, and demonstrate its precision in simulations. The variance of the estimation links directly to previously explored perturbation of the analytic eigenvalues and eigenspaces of a parahermitian cross-spectral density matrix when estimated from finite data.

KW - space-time covariance

KW - estimation

KW - parahermitian matrix EVD

KW - polynomial matrices

M3 - Paper

SP - 8033

EP - 8037

T2 - 2019 International Conference on Acoustics, Speech, and Signal Processing

Y2 - 12 May 2019 through 17 May 2019

ER -

Sample space-time covariance matrix estimation

Abstract

Conference

Keywords

Access to Document

Fingerprint

Projects

Signal Processing in the Information Age (UDRC III)

Research output

Space-time covariance matrix estimation: loss of algebraic multiplicities of Eigenvalues

Cite this