Metrics for multivariate power spectra

Lipeng Ning; Xianhua Jiang; Tryphon T Georgiou

doi:10.1109/CDC.2012.6426046

Metrics for multivariate power spectra

Lipeng Ning, Xianhua Jiang, Tryphon T Georgiou

Electrical and Computer Engineering

Research output: Contribution to journal › Conference article › peer-review

1 Scopus citations

Abstract

This paper builds on earlier work in [1] on metrics for power spectral densities (PSD) of multivariable time-series. We present an approach to quantify dissimilarities aimed at optimal prediction and smoothing. Divergence measures are constructed based on the degradation of prediction-error and smoothing-error variances. These induce Riemannian metrics which generalize earlier results for scalar PSD's.

Original language	English (US)
Article number	6426046
Pages (from-to)	4727-4732
Number of pages	6
Journal	Proceedings of the IEEE Conference on Decision and Control
DOIs	https://doi.org/10.1109/CDC.2012.6426046
State	Published - 2012
Event	51st IEEE Conference on Decision and Control, CDC 2012 - Maui, HI, United States Duration: Dec 10 2012 → Dec 13 2012

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10.1109/CDC.2012.6426046

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title = "Metrics for multivariate power spectra",

abstract = "This paper builds on earlier work in [1] on metrics for power spectral densities (PSD) of multivariable time-series. We present an approach to quantify dissimilarities aimed at optimal prediction and smoothing. Divergence measures are constructed based on the degradation of prediction-error and smoothing-error variances. These induce Riemannian metrics which generalize earlier results for scalar PSD's.",

author = "Lipeng Ning and Xianhua Jiang and Georgiou, {Tryphon T}",

year = "2012",

doi = "10.1109/CDC.2012.6426046",

language = "English (US)",

pages = "4727--4732",

journal = "Proceedings of the IEEE Conference on Decision and Control",

issn = "0743-1546",

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note = "51st IEEE Conference on Decision and Control, CDC 2012 ; Conference date: 10-12-2012 Through 13-12-2012",

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T1 - Metrics for multivariate power spectra

AU - Ning, Lipeng

AU - Jiang, Xianhua

AU - Georgiou, Tryphon T

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AB - This paper builds on earlier work in [1] on metrics for power spectral densities (PSD) of multivariable time-series. We present an approach to quantify dissimilarities aimed at optimal prediction and smoothing. Divergence measures are constructed based on the degradation of prediction-error and smoothing-error variances. These induce Riemannian metrics which generalize earlier results for scalar PSD's.

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U2 - 10.1109/CDC.2012.6426046

DO - 10.1109/CDC.2012.6426046

M3 - Conference article

AN - SCOPUS:84874250847

SN - 0743-1546

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JO - Proceedings of the IEEE Conference on Decision and Control

JF - Proceedings of the IEEE Conference on Decision and Control

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T2 - 51st IEEE Conference on Decision and Control, CDC 2012

Y2 - 10 December 2012 through 13 December 2012

ER -

Metrics for multivariate power spectra

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