DocumentCode
3163809
Title
Metrics for multivariate power spectra
Author
Lipeng Ning ; Xianhua Jiang ; Georgiou, Tryphon T.
Author_Institution
Dept. of Electr. & Comput. Eng., Univ. of Minnesota, Minneapolis, MN, USA
fYear
2012
fDate
10-13 Dec. 2012
Firstpage
4727
Lastpage
4732
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.
Keywords
time series; PSD; Riemannian metrics; divergence measures; multivariable time-series; multivariate power spectra; optimal prediction; power spectral densities; prediction-error variances; smoothing-error variances; Degradation; Geometry; Power measurement; Smoothing methods; Spectral analysis; Technological innovation;
fLanguage
English
Publisher
ieee
Conference_Titel
Decision and Control (CDC), 2012 IEEE 51st Annual Conference on
Conference_Location
Maui, HI
ISSN
0743-1546
Print_ISBN
978-1-4673-2065-8
Electronic_ISBN
0743-1546
Type
conf
DOI
10.1109/CDC.2012.6426046
Filename
6426046
Link To Document