Generalizations of Nonanticipative Rate Distortion Function to Multivariate Nonstationary Gaussian Autoregressive Processes

Charalambous, Charalambos D.; Kourtellaris, Christos; Charalambous, Themistoklis; Van Schuppen, Jan H.

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Generalizations of Nonanticipative Rate Distortion Function to Multivariate Nonstationary Gaussian Autoregressive Processes

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Title:	Generalizations of Nonanticipative Rate Distortion Function to Multivariate Nonstationary Gaussian Autoregressive Processes
Author(s):	Charalambous, Charalambos D. ; Kourtellaris, Christos ; Charalambous, Themistoklis ; Van Schuppen, Jan H.
Date:	2019-12-01
Language:	en
Pages:	6 8190-8195
Department:	University of Cyprus Distributed and Networked Control Systems Van Schuppen Control Research Department of Electrical Engineering and Automation
ISBN:	9781728113982
Series:	Proceedings of the 58th IEEE Conference on Decision and Control, CDC 2019, Proceedings of the IEEE Conference on Decision and Control, Volume 2019-December
ISSN:	0743-1546
DOI-number:	10.1109/CDC40024.2019.9029859
Keywords:	Distortion, Decoding, Entropy, Electronic mail, Markov processes, Symmetric matrices, RNA
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Citation: Charalambous , C D , Kourtellaris , C , Charalambous , T & Van Schuppen , J H 2019 , Generalizations of Nonanticipative Rate Distortion Function to Multivariate Nonstationary Gaussian Autoregressive Processes . in Proceedings of the 58th IEEE Conference on Decision and Control, CDC 2019 . , 9029859 , Proceedings of the IEEE Conference on Decision and Control , vol. 2019-December , IEEE , pp. 8190-8195 , IEEE Conference on Decision and Control , Nice , France , 11/12/2019 . https://doi.org/10.1109/CDC40024.2019.9029859
Abstract: The characterizations of nonanticipative rate distortion function (NRDF) on a finite horizon are generalized to nonstationary multivariate Gaussian order L autoregressive, AR(L), source processes, with respect to mean square error (MSE) distortion functions. It is shown that the optimal reproduction distributions are induced by a reproduction process, which is a linear function of the state of the source, its best mean-square error estimate, and a Gaussian random process.

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