Reducing the Computational Complexity of Reconstruction in Compressed Sensing Nonuniform Sampling

Research output: Contribution to journalConference article in JournalResearchpeer-review

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Abstract

This paper proposes a method that reduces the computational complexity of signal reconstruction in single-channel nonuniform sampling while acquiring frequency sparse multi-band signals. Generally, this compressed sensing based signal acquisition allows a decrease in the sampling rate of frequency sparse signals, but requires computationally expensive reconstruction algorithms. This can be an obstacle for real-time applications. The reduction of complexity is achieved by applying a multi-coset sampling procedure. This proposed method reduces the size of the dictionary matrix, the size of the measurement matrix and the number of iterations of the reconstruction algorithm in comparison to the direct single-channel approach. We consider an orthogonal matching pursuit reconstruction algorithm for single-channel sampling and its modification for multi-coset sampling. Theoretical as well as numerical analyses demonstrate order of magnitude reduction in execution time for typical problem sizes without degradation of the signal reconstruction quality.
Original languageEnglish
JournalEuropean Signal Processing Conference (EUSIPCO)
Volume21
Number of pages5
ISSN2076-1465
Publication statusPublished - 2013
EventEuropean Signal Processing Conference EUSIPCO 2013 - Marrakech, Moroco
Duration: 9 Sep 201313 Sep 2013

Conference

ConferenceEuropean Signal Processing Conference EUSIPCO 2013
CityMarrakech, Moroco
Period09/09/201313/09/2013

Fingerprint

Compressed sensing
Computational complexity
Sampling
Signal reconstruction
Glossaries
Degradation

Keywords

  • compressed sensing
  • multi-coset
  • sampling
  • nonuniform sampling
  • reconstruction algorithm

Cite this

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title = "Reducing the Computational Complexity of Reconstruction in Compressed Sensing Nonuniform Sampling",
abstract = "This paper proposes a method that reduces the computational complexity of signal reconstruction in single-channel nonuniform sampling while acquiring frequency sparse multi-band signals. Generally, this compressed sensing based signal acquisition allows a decrease in the sampling rate of frequency sparse signals, but requires computationally expensive reconstruction algorithms. This can be an obstacle for real-time applications. The reduction of complexity is achieved by applying a multi-coset sampling procedure. This proposed method reduces the size of the dictionary matrix, the size of the measurement matrix and the number of iterations of the reconstruction algorithm in comparison to the direct single-channel approach. We consider an orthogonal matching pursuit reconstruction algorithm for single-channel sampling and its modification for multi-coset sampling. Theoretical as well as numerical analyses demonstrate order of magnitude reduction in execution time for typical problem sizes without degradation of the signal reconstruction quality.",
keywords = "compressed sensing, multi-coset, sampling, nonuniform sampling, reconstruction algorithm",
author = "Ruben Grigoryan and Jensen, {Tobias Lindstr{\o}m} and Thomas Arildsen and Torben Larsen",
year = "2013",
language = "English",
volume = "21",
journal = "Proceedings of the European Signal Processing Conference",
issn = "2076-1465",
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}

Reducing the Computational Complexity of Reconstruction in Compressed Sensing Nonuniform Sampling. / Grigoryan, Ruben; Jensen, Tobias Lindstrøm; Arildsen, Thomas; Larsen, Torben.

In: European Signal Processing Conference (EUSIPCO), Vol. 21, 2013.

Research output: Contribution to journalConference article in JournalResearchpeer-review

TY - GEN

T1 - Reducing the Computational Complexity of Reconstruction in Compressed Sensing Nonuniform Sampling

AU - Grigoryan, Ruben

AU - Jensen, Tobias Lindstrøm

AU - Arildsen, Thomas

AU - Larsen, Torben

PY - 2013

Y1 - 2013

N2 - This paper proposes a method that reduces the computational complexity of signal reconstruction in single-channel nonuniform sampling while acquiring frequency sparse multi-band signals. Generally, this compressed sensing based signal acquisition allows a decrease in the sampling rate of frequency sparse signals, but requires computationally expensive reconstruction algorithms. This can be an obstacle for real-time applications. The reduction of complexity is achieved by applying a multi-coset sampling procedure. This proposed method reduces the size of the dictionary matrix, the size of the measurement matrix and the number of iterations of the reconstruction algorithm in comparison to the direct single-channel approach. We consider an orthogonal matching pursuit reconstruction algorithm for single-channel sampling and its modification for multi-coset sampling. Theoretical as well as numerical analyses demonstrate order of magnitude reduction in execution time for typical problem sizes without degradation of the signal reconstruction quality.

AB - This paper proposes a method that reduces the computational complexity of signal reconstruction in single-channel nonuniform sampling while acquiring frequency sparse multi-band signals. Generally, this compressed sensing based signal acquisition allows a decrease in the sampling rate of frequency sparse signals, but requires computationally expensive reconstruction algorithms. This can be an obstacle for real-time applications. The reduction of complexity is achieved by applying a multi-coset sampling procedure. This proposed method reduces the size of the dictionary matrix, the size of the measurement matrix and the number of iterations of the reconstruction algorithm in comparison to the direct single-channel approach. We consider an orthogonal matching pursuit reconstruction algorithm for single-channel sampling and its modification for multi-coset sampling. Theoretical as well as numerical analyses demonstrate order of magnitude reduction in execution time for typical problem sizes without degradation of the signal reconstruction quality.

KW - compressed sensing

KW - multi-coset

KW - sampling

KW - nonuniform sampling

KW - reconstruction algorithm

M3 - Conference article in Journal

VL - 21

JO - Proceedings of the European Signal Processing Conference

JF - Proceedings of the European Signal Processing Conference

SN - 2076-1465

ER -