Support estimation of a sample space-time covariance matrix

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

Research output: Contribution to conferencePaper

Abstract

The ensemble-optimum support for a sample space-time covariance matrix can be determined from the ground truth space-time covariance, and the variance of the estimator. In this paper we provide approximations that permit the estimation of the sample-optimum support from the estimate itself, given a suitable detection threshold. In simulations, we provide some insight into the (in)sensitivity and dependencies of this threshold.
LanguageEnglish
Number of pages5
Publication statusAccepted/In press - 1 Mar 2019
EventSensor Signal Processing for Defence 2019 - Brighton, United Kingdom
Duration: 9 May 201910 May 2019

Conference

ConferenceSensor Signal Processing for Defence 2019
Abbreviated titleSSPD'19
CountryUnited Kingdom
CityBrighton
Period9/05/1910/05/19

Fingerprint

Covariance matrix

Keywords

  • space-time covariance matrix
  • parahermitian matrix
  • cross-correlation
  • estimation

Cite this

Delaosa, C., Pestana, J., Goddard, N. J., Somasundaram, S. D., & Weiss, S. (Accepted/In press). Support estimation of a sample space-time covariance matrix. Paper presented at Sensor Signal Processing for Defence 2019, Brighton, United Kingdom.
Delaosa, Connor ; Pestana, Jennifer ; Goddard, Nicholas J. ; Somasundaram, Samuel D. ; Weiss, Stephan. / Support estimation of a sample space-time covariance matrix. Paper presented at Sensor Signal Processing for Defence 2019, Brighton, United Kingdom.5 p.
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abstract = "The ensemble-optimum support for a sample space-time covariance matrix can be determined from the ground truth space-time covariance, and the variance of the estimator. In this paper we provide approximations that permit the estimation of the sample-optimum support from the estimate itself, given a suitable detection threshold. In simulations, we provide some insight into the (in)sensitivity and dependencies of this threshold.",
keywords = "space-time covariance matrix, parahermitian matrix, cross-correlation, estimation",
author = "Connor Delaosa and Jennifer Pestana and Goddard, {Nicholas J.} and Somasundaram, {Samuel D.} and Stephan Weiss",
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Delaosa, C, Pestana, J, Goddard, NJ, Somasundaram, SD & Weiss, S 2019, 'Support estimation of a sample space-time covariance matrix' Paper presented at Sensor Signal Processing for Defence 2019, Brighton, United Kingdom, 9/05/19 - 10/05/19, .

Support estimation of a sample space-time covariance matrix. / Delaosa, Connor; Pestana, Jennifer; Goddard, Nicholas J.; Somasundaram, Samuel D.; Weiss, Stephan.

2019. Paper presented at Sensor Signal Processing for Defence 2019, Brighton, United Kingdom.

Research output: Contribution to conferencePaper

TY - CONF

T1 - Support estimation of a sample space-time covariance matrix

AU - Delaosa, Connor

AU - Pestana, Jennifer

AU - Goddard, Nicholas J.

AU - Somasundaram, Samuel D.

AU - Weiss, Stephan

N1 - © 2019 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.

PY - 2019/3/1

Y1 - 2019/3/1

N2 - The ensemble-optimum support for a sample space-time covariance matrix can be determined from the ground truth space-time covariance, and the variance of the estimator. In this paper we provide approximations that permit the estimation of the sample-optimum support from the estimate itself, given a suitable detection threshold. In simulations, we provide some insight into the (in)sensitivity and dependencies of this threshold.

AB - The ensemble-optimum support for a sample space-time covariance matrix can be determined from the ground truth space-time covariance, and the variance of the estimator. In this paper we provide approximations that permit the estimation of the sample-optimum support from the estimate itself, given a suitable detection threshold. In simulations, we provide some insight into the (in)sensitivity and dependencies of this threshold.

KW - space-time covariance matrix

KW - parahermitian matrix

KW - cross-correlation

KW - estimation

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

M3 - Paper

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Delaosa C, Pestana J, Goddard NJ, Somasundaram SD, Weiss S. Support estimation of a sample space-time covariance matrix. 2019. Paper presented at Sensor Signal Processing for Defence 2019, Brighton, United Kingdom.