Sparse system identification using orthogonal rational functions

Xiong Dan; Chai Li; Jingxin Zhang

doi:10.1109/WCICA.2014.7053087

Sparse system identification using orthogonal rational functions

Xiong Dan, Chai Li, Jingxin Zhang

Electrical and Computer Systems Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

9 Citations (Scopus)

Abstract

We consider the problem of reconstructing a real sparse coefficient θ of a transfer function under the orthogonal rational functions from a limit number of linear measurements. Given m randomly selected samples of Φθ, where Φ is a sample matrix constructed by orthogonal rational functions at samples in the unit circle, we show that ℓ1 minimization can recover the coefficient θ from the real part model or the imaginary part model for the real and imaginary part of Φ have the similar structure of the orthogonal matrix.

Original language	English
Title of host publication	2014 11th World Congress on Intelligent Control and Automation (WCICA)
Editors	Hong Wang
Place of Publication	Piscataway NJ USA
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	2340 - 2345
Number of pages	6
ISBN (Print)	9781479958269
DOIs	https://doi.org/10.1109/WCICA.2014.7053087
Publication status	Published - 2015
Event	World Congress on Intelligent Control and Automation 2014 - Shenyang, China Duration: 29 Jun 2014 → 4 Jul 2014 Conference number: 11th

Conference

Conference	World Congress on Intelligent Control and Automation 2014
Abbreviated title	WCICA 2014
Country/Territory	China
City	Shenyang
Period	29/06/14 → 4/07/14

Keywords

Compressed sensing
Orthogonal rational function
System identification
TM basis

Access to Document

10.1109/WCICA.2014.7053087

Cite this

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title = "Sparse system identification using orthogonal rational functions",

abstract = "We consider the problem of reconstructing a real sparse coefficient θ of a transfer function under the orthogonal rational functions from a limit number of linear measurements. Given m randomly selected samples of Φθ, where Φ is a sample matrix constructed by orthogonal rational functions at samples in the unit circle, we show that ℓ1 minimization can recover the coefficient θ from the real part model or the imaginary part model for the real and imaginary part of Φ have the similar structure of the orthogonal matrix.",

keywords = "Compressed sensing, Orthogonal rational function, System identification, TM basis",

author = "Xiong Dan and Chai Li and Jingxin Zhang",

year = "2015",

doi = "10.1109/WCICA.2014.7053087",

language = "English",

isbn = "9781479958269",

pages = "2340 -- 2345",

editor = "Hong Wang",

booktitle = "2014 11th World Congress on Intelligent Control and Automation (WCICA)",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States of America",

note = "World Congress on Intelligent Control and Automation 2014, WCICA 2014 ; Conference date: 29-06-2014 Through 04-07-2014",

}

Dan, X, Li, C & Zhang, J 2015, Sparse system identification using orthogonal rational functions. in H Wang (ed.), 2014 11th World Congress on Intelligent Control and Automation (WCICA). IEEE, Institute of Electrical and Electronics Engineers, Piscataway NJ USA, pp. 2340 - 2345, World Congress on Intelligent Control and Automation 2014, Shenyang, China, 29/06/14. https://doi.org/10.1109/WCICA.2014.7053087

Sparse system identification using orthogonal rational functions. / Dan, Xiong; Li, Chai; Zhang, Jingxin.
2014 11th World Congress on Intelligent Control and Automation (WCICA). ed. / Hong Wang. Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers, 2015. p. 2340 - 2345.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

TY - GEN

T1 - Sparse system identification using orthogonal rational functions

AU - Dan, Xiong

AU - Li, Chai

AU - Zhang, Jingxin

N1 - Conference code: 11th

PY - 2015

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N2 - We consider the problem of reconstructing a real sparse coefficient θ of a transfer function under the orthogonal rational functions from a limit number of linear measurements. Given m randomly selected samples of Φθ, where Φ is a sample matrix constructed by orthogonal rational functions at samples in the unit circle, we show that ℓ1 minimization can recover the coefficient θ from the real part model or the imaginary part model for the real and imaginary part of Φ have the similar structure of the orthogonal matrix.

AB - We consider the problem of reconstructing a real sparse coefficient θ of a transfer function under the orthogonal rational functions from a limit number of linear measurements. Given m randomly selected samples of Φθ, where Φ is a sample matrix constructed by orthogonal rational functions at samples in the unit circle, we show that ℓ1 minimization can recover the coefficient θ from the real part model or the imaginary part model for the real and imaginary part of Φ have the similar structure of the orthogonal matrix.

KW - Compressed sensing

KW - Orthogonal rational function

KW - System identification

KW - TM basis

UR - https://www.scopus.com/pages/publications/84932080576

U2 - 10.1109/WCICA.2014.7053087

DO - 10.1109/WCICA.2014.7053087

M3 - Conference Paper

SN - 9781479958269

SP - 2340

EP - 2345

BT - 2014 11th World Congress on Intelligent Control and Automation (WCICA)

A2 - Wang, Hong

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - Piscataway NJ USA

T2 - World Congress on Intelligent Control and Automation 2014

Y2 - 29 June 2014 through 4 July 2014

ER -

Sparse system identification using orthogonal rational functions

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