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

Department of Electronic Systems

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

12 Citations (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.

Original language	English
Title of host publication	2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings
Publisher	IEEE Press
Publication date	1 Dec 2013
Pages	624
Article number	6736961
ISBN (Print)	978-1-4799-0248-4
DOIs	https://doi.org/10.1109/GlobalSIP.2013.6736961
Publication status	Published - 1 Dec 2013
Event	1st IEEE Global Conference on Signal and Information Processing - Austin, TX, United States Duration: 3 Dec 2013 → 5 Dec 2013 http://www.ieeeglobalsip.org/2013/

Conference

Conference	1st IEEE Global Conference on Signal and Information Processing
Country/Territory	United States
City	Austin, TX
Period	03/12/2013 → 05/12/2013
Internet address	http://www.ieeeglobalsip.org/2013/

Keywords

Compressive sensing
Interpolation
Parameter estimation
Time delay estimation

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10.1109/GlobalSIP.2013.6736961

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Cite this

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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.",

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Fyhn, K, Jensen, SH & Duarte, MF 2013, Compressive time delay estimation using interpolation. in 2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings., 6736961, IEEE Press, pp. 624, 1st IEEE Global Conference on Signal and Information Processing, Austin, TX, United States, 03/12/2013. https://doi.org/10.1109/GlobalSIP.2013.6736961

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. p. 624 6736961.

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › 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

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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

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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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