Compressive time delay estimation using interpolation

Karsten Fyhn; Soren Holdt Jensen; Marco F. Duarte

doi:10.1109/GlobalSIP.2013.6736961

Compressive time delay estimation using interpolation

Karsten Fyhn, Soren Holdt Jensen, Marco F. Duarte

Institut for Elektroniske Systemer

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review

12 Citationer (Scopus)

Abstract

We show that compressive sensing (CS) applied to time delay estimation (TDE) simultaneously enables a reduction in the sampling frequency and preserves good estimation precision. With CS, we seek to recover signals and parameters from an under-determined system of linear equations by assuming sparsity in a known dictionary. A common problem in CS is that the observed signals may not be sparsely representable in the dictionary. This problem also occurs in TDE as the delay parameter is a continuous parameter. We remedy this issue by combining CS with interpolation.

Originalsprog	Engelsk
Titel	2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings
Forlag	IEEE Press
Publikationsdato	1 dec. 2013
Sider	624
Artikelnummer	6736961
ISBN (Trykt)	978-1-4799-0248-4
DOI	https://doi.org/10.1109/GlobalSIP.2013.6736961
Status	Udgivet - 1 dec. 2013
Begivenhed	1st IEEE Global Conference on Signal and Information Processing - Austin, TX, USA Varighed: 3 dec. 2013 → 5 dec. 2013 http://www.ieeeglobalsip.org/2013/

Konference

Konference	1st IEEE Global Conference on Signal and Information Processing
Land/Område	USA
By	Austin, TX
Periode	03/12/2013 → 05/12/2013
Internetadresse	http://www.ieeeglobalsip.org/2013/

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10.1109/GlobalSIP.2013.6736961

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Citationsformater

@inproceedings{aa6178faab7a4c8b88c58665fb359d05,

title = "Compressive time delay estimation using interpolation",

abstract = "We show that compressive sensing (CS) applied to time delay estimation (TDE) simultaneously enables a reduction in the sampling frequency and preserves good estimation precision. With CS, we seek to recover signals and parameters from an under-determined system of linear equations by assuming sparsity in a known dictionary. A common problem in CS is that the observed signals may not be sparsely representable in the dictionary. This problem also occurs in TDE as the delay parameter is a continuous parameter. We remedy this issue by combining CS with interpolation.",

keywords = "Compressive sensing, Interpolation, Parameter estimation, Time delay estimation",

author = "Karsten Fyhn and Jensen, {Soren Holdt} and Duarte, {Marco F.}",

year = "2013",

month = dec,

day = "1",

doi = "10.1109/GlobalSIP.2013.6736961",

language = "English",

isbn = "978-1-4799-0248-4",

pages = "624",

booktitle = "2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings",

publisher = "IEEE Press",

note = "1st IEEE Global Conference on Signal and Information Processing ; Conference date: 03-12-2013 Through 05-12-2013",

url = "http://www.ieeeglobalsip.org/2013/",

}

Compressive time delay estimation using interpolation. / Fyhn, Karsten; Jensen, Soren Holdt; Duarte, Marco F.
2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings. IEEE Press, 2013. s. 624 6736961.

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review

TY - GEN

T1 - Compressive time delay estimation using interpolation

AU - Fyhn, Karsten

AU - Jensen, Soren Holdt

AU - Duarte, Marco F.

PY - 2013/12/1

Y1 - 2013/12/1

N2 - We show that compressive sensing (CS) applied to time delay estimation (TDE) simultaneously enables a reduction in the sampling frequency and preserves good estimation precision. With CS, we seek to recover signals and parameters from an under-determined system of linear equations by assuming sparsity in a known dictionary. A common problem in CS is that the observed signals may not be sparsely representable in the dictionary. This problem also occurs in TDE as the delay parameter is a continuous parameter. We remedy this issue by combining CS with interpolation.

AB - We show that compressive sensing (CS) applied to time delay estimation (TDE) simultaneously enables a reduction in the sampling frequency and preserves good estimation precision. With CS, we seek to recover signals and parameters from an under-determined system of linear equations by assuming sparsity in a known dictionary. A common problem in CS is that the observed signals may not be sparsely representable in the dictionary. This problem also occurs in TDE as the delay parameter is a continuous parameter. We remedy this issue by combining CS with interpolation.

KW - Compressive sensing

KW - Interpolation

KW - Parameter estimation

KW - Time delay estimation

U2 - 10.1109/GlobalSIP.2013.6736961

DO - 10.1109/GlobalSIP.2013.6736961

M3 - Article in proceeding

AN - SCOPUS:84897677414

SN - 978-1-4799-0248-4

SP - 624

BT - 2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings

PB - IEEE Press

T2 - 1st IEEE Global Conference on Signal and Information Processing

Y2 - 3 December 2013 through 5 December 2013

ER -

Compressive time delay estimation using interpolation

Abstract

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