Signal reconstruction with generalized sampling
(2018) 56th IEEE Annual Conference on Decision and Control, CDC 2017 p.6253-6258- Abstract
- This paper studies the problem of reconstructing continuous-time signals from discrete-time uniformly sampled data. This signal reconstruction problem has been studied by the authors in various contexts, and led to a new signal processing paradigm. The crux there is to employ a physically realizable signal generator model, and design an (sub)optimal filter via H-infinity (C+) optimal sampled-data control theory. The present paper extends this framework to the situation where sampling is more general having a generalized sampling kernel. It is more consistent with a more general framework, for example, wavelet signal expansion, and can lead to a more general applications. We give a general setup along with a solution via... (More)
- This paper studies the problem of reconstructing continuous-time signals from discrete-time uniformly sampled data. This signal reconstruction problem has been studied by the authors in various contexts, and led to a new signal processing paradigm. The crux there is to employ a physically realizable signal generator model, and design an (sub)optimal filter via H-infinity (C+) optimal sampled-data control theory. The present paper extends this framework to the situation where sampling is more general having a generalized sampling kernel. It is more consistent with a more general framework, for example, wavelet signal expansion, and can lead to a more general applications. We give a general setup along with a solution via fast-sample/fast-hold approximation. A simulation is presented to illustrate the result. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/d99c17e4-324c-4eb4-aebf-54dda9ab7cd8
- author
- Yamamoto, Kaoru LU ; Nagahara, Masaaki and Yamamoto, Yutaka
- organization
- publishing date
- 2018-01
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 56th IEEE Conference on Decision and Control, CDC, 2017
- pages
- 6 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 56th IEEE Annual Conference on Decision and Control, CDC 2017
- conference location
- Melbourne, Australia
- conference dates
- 2017-12-12 - 2017-12-15
- external identifiers
-
- scopus:85046161591
- DOI
- 10.1109/CDC.2017.8264601
- language
- English
- LU publication?
- yes
- id
- d99c17e4-324c-4eb4-aebf-54dda9ab7cd8
- date added to LUP
- 2018-02-16 12:17:34
- date last changed
- 2022-04-25 05:33:33
@inproceedings{d99c17e4-324c-4eb4-aebf-54dda9ab7cd8,
abstract = {{This paper studies the problem of reconstructing continuous-time signals from discrete-time uniformly sampled data. This signal reconstruction problem has been studied by the authors in various contexts, and led to a new signal processing paradigm. The crux there is to employ a physically realizable signal generator model, and design an (sub)optimal filter via H-infinity (C+) optimal sampled-data control theory. The present paper extends this framework to the situation where sampling is more general having a generalized sampling kernel. It is more consistent with a more general framework, for example, wavelet signal expansion, and can lead to a more general applications. We give a general setup along with a solution via fast-sample/fast-hold approximation. A simulation is presented to illustrate the result.}},
author = {{Yamamoto, Kaoru and Nagahara, Masaaki and Yamamoto, Yutaka}},
booktitle = {{56th IEEE Conference on Decision and Control, CDC, 2017}},
language = {{eng}},
pages = {{6253--6258}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
title = {{Signal reconstruction with generalized sampling}},
url = {{https://lup.lub.lu.se/search/files/38612881/KY_NM_YY_CDC17_pre.pdf}},
doi = {{10.1109/CDC.2017.8264601}},
year = {{2018}},
}