Detection of time-varying support via rank evolution approach for effective joint sparse recovery

A. Lavrenko; F. Romer; G. Del Galdo; R. Thoma; O. Arikan

doi:10.1109/EUSIPCO.2015.7362677

Detection of time-varying support via rank evolution approach for effective joint sparse recovery

A. Lavrenko, F. Romer, G. Del Galdo, R. Thoma, O. Arikan

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

4 Citations (Scopus)

Abstract

Efficient recovery of sparse signals from few linear projections is a primary goal in a number of applications, most notably in a recently-emerged area of compressed sensing. The multiple measurement vector (MMV) joint sparse recovery is an extension of the single vector sparse recovery problem to the case when a set of consequent measurements share the same support. In this contribution we consider a modification of the MMV problem where the signal support can change from one block of data to another and the moment of change is not known in advance. We propose an approach for the support change detection based on the sequential rank estimation of a windowed block of the measurement data. We show that under certain conditions it allows for an unambiguous determination of the moment of change, provided that the consequent data vectors are incoherent to each other.

Original language	English
Title of host publication	2015 23rd European Signal Processing Conference, EUSIPCO 2015
Place of Publication	Piscataway, NJ
Publisher	IEEE
Pages	1716-1720
Number of pages	5
ISBN (Electronic)	978-0-9928-6263-3
DOIs	https://doi.org/10.1109/EUSIPCO.2015.7362677
Publication status	Published - 22 Dec 2015
Externally published	Yes
Event	23rd European Signal Processing Conference, EUSIPCO 2015 - Nice, France Duration: 31 Aug 2015 → 4 Sept 2015 Conference number: 23 http://www.eusipco2015.org/

Publication series

Name	2015 23rd European Signal Processing Conference, EUSIPCO 2015
Publisher	IEEE
Number	23
Volume	2015
ISSN (Print)	2076-1465

Conference

Conference	23rd European Signal Processing Conference, EUSIPCO 2015
Abbreviated title	EUSIPCO
Country/Territory	France
City	Nice
Period	31/08/15 → 4/09/15
Internet address	http://www.eusipco2015.org/

Keywords

Multiple measurement vector
Sparse recovery
Stationarity window
Time-varying support
n/a OA procedure

Access to Document

10.1109/EUSIPCO.2015.7362677

Cite this

Lavrenko, A., Romer, F., Del Galdo, G., Thoma, R., & Arikan, O. (2015). Detection of time-varying support via rank evolution approach for effective joint sparse recovery. In 2015 23rd European Signal Processing Conference, EUSIPCO 2015 (pp. 1716-1720). Article 7362677 (2015 23rd European Signal Processing Conference, EUSIPCO 2015; Vol. 2015, No. 23). IEEE. https://doi.org/10.1109/EUSIPCO.2015.7362677

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title = "Detection of time-varying support via rank evolution approach for effective joint sparse recovery",

abstract = "Efficient recovery of sparse signals from few linear projections is a primary goal in a number of applications, most notably in a recently-emerged area of compressed sensing. The multiple measurement vector (MMV) joint sparse recovery is an extension of the single vector sparse recovery problem to the case when a set of consequent measurements share the same support. In this contribution we consider a modification of the MMV problem where the signal support can change from one block of data to another and the moment of change is not known in advance. We propose an approach for the support change detection based on the sequential rank estimation of a windowed block of the measurement data. We show that under certain conditions it allows for an unambiguous determination of the moment of change, provided that the consequent data vectors are incoherent to each other.",

keywords = "Multiple measurement vector, Sparse recovery, Stationarity window, Time-varying support, n/a OA procedure",

author = "A. Lavrenko and F. Romer and {Del Galdo}, G. and R. Thoma and O. Arikan",

note = "Publisher Copyright: {\textcopyright} 2015 EURASIP.; 23rd European Signal Processing Conference, EUSIPCO 2015, EUSIPCO ; Conference date: 31-08-2015 Through 04-09-2015",

year = "2015",

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doi = "10.1109/EUSIPCO.2015.7362677",

language = "English",

series = "2015 23rd European Signal Processing Conference, EUSIPCO 2015",

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Lavrenko, A, Romer, F, Del Galdo, G, Thoma, R & Arikan, O 2015, Detection of time-varying support via rank evolution approach for effective joint sparse recovery. in 2015 23rd European Signal Processing Conference, EUSIPCO 2015., 7362677, 2015 23rd European Signal Processing Conference, EUSIPCO 2015, no. 23, vol. 2015, IEEE, Piscataway, NJ, pp. 1716-1720, 23rd European Signal Processing Conference, EUSIPCO 2015, Nice, France, 31/08/15. https://doi.org/10.1109/EUSIPCO.2015.7362677

Detection of time-varying support via rank evolution approach for effective joint sparse recovery. / Lavrenko, A.; Romer, F.; Del Galdo, G. et al.
2015 23rd European Signal Processing Conference, EUSIPCO 2015. Piscataway, NJ: IEEE, 2015. p. 1716-1720 7362677 (2015 23rd European Signal Processing Conference, EUSIPCO 2015; Vol. 2015, No. 23).

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

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T1 - Detection of time-varying support via rank evolution approach for effective joint sparse recovery

AU - Lavrenko, A.

AU - Romer, F.

AU - Del Galdo, G.

AU - Thoma, R.

AU - Arikan, O.

N1 - Conference code: 23

PY - 2015/12/22

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N2 - Efficient recovery of sparse signals from few linear projections is a primary goal in a number of applications, most notably in a recently-emerged area of compressed sensing. The multiple measurement vector (MMV) joint sparse recovery is an extension of the single vector sparse recovery problem to the case when a set of consequent measurements share the same support. In this contribution we consider a modification of the MMV problem where the signal support can change from one block of data to another and the moment of change is not known in advance. We propose an approach for the support change detection based on the sequential rank estimation of a windowed block of the measurement data. We show that under certain conditions it allows for an unambiguous determination of the moment of change, provided that the consequent data vectors are incoherent to each other.

AB - Efficient recovery of sparse signals from few linear projections is a primary goal in a number of applications, most notably in a recently-emerged area of compressed sensing. The multiple measurement vector (MMV) joint sparse recovery is an extension of the single vector sparse recovery problem to the case when a set of consequent measurements share the same support. In this contribution we consider a modification of the MMV problem where the signal support can change from one block of data to another and the moment of change is not known in advance. We propose an approach for the support change detection based on the sequential rank estimation of a windowed block of the measurement data. We show that under certain conditions it allows for an unambiguous determination of the moment of change, provided that the consequent data vectors are incoherent to each other.

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Lavrenko A, Romer F, Del Galdo G, Thoma R, Arikan O. Detection of time-varying support via rank evolution approach for effective joint sparse recovery. In 2015 23rd European Signal Processing Conference, EUSIPCO 2015. Piscataway, NJ: IEEE. 2015. p. 1716-1720. 7362677. (2015 23rd European Signal Processing Conference, EUSIPCO 2015; 23). doi: 10.1109/EUSIPCO.2015.7362677

Detection of time-varying support via rank evolution approach for effective joint sparse recovery

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