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Compressed Sensing: Algorithms and Applications
KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
2012 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

The theoretical problem of finding the solution to an underdeterminedset of linear equations has for several years attracted considerable attentionin the literature. This problem has many practical applications.One example of such an application is compressed sensing (cs), whichhas the potential to revolutionize how we acquire and process signals. Ina general cs setup, few measurement coefficients are available and thetask is to reconstruct a larger, sparse signal.In this thesis we focus on algorithm design and selected applicationsfor cs. The contributions of the thesis appear in the following order:(1) We study an application where cs can be used to relax the necessityof fast sampling for power spectral density estimation problems. Inthis application we show by experimental evaluation that we can gainan order of magnitude in reduced sampling frequency. (2) In order toimprove cs recovery performance, we extend simple well-known recoveryalgorithms by introducing a look-ahead concept. From simulations it isobserved that the additional complexity results in significant improvementsin recovery performance. (3) For sensor networks, we extend thecurrent framework of cs by introducing a new general network modelwhich is suitable for modeling several cs sensor nodes with correlatedmeasurements. Using this signal model we then develop several centralizedand distributed cs recovery algorithms. We find that both thecentralized and distributed algorithms achieve a significant gain in recoveryperformance compared to the standard, disconnected, algorithms.For the distributed case, we also see that as the network connectivity increases,the performance rapidly converges to the performance of thecentralized solution.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2012. , ix, 35 p.
Series
Trita-EE, ISSN 1653-5146
Keyword [en]
compressed sensing, greedy pursuits, subspace pursuit, orthogonal matching pursuit, power spectral density estimation, distributed compressed sensing
National Category
Telecommunications
Identifiers
URN: urn:nbn:se:kth:diva-90074ISBN: 978-91-7501-269-8 (print)OAI: oai:DiVA.org:kth-90074DiVA: diva2:504064
Presentation
2012-03-09, Q2, KTH, Osquldas väg 10, Stockholm, 15:58 (English)
Opponent
Supervisors
Funder
ICT - The Next Generation
Note

QC 20120229

Available from: 2012-02-29 Created: 2012-02-17 Last updated: 2013-04-15Bibliographically approved
List of papers
1. On the use of Compressive Sampling for Wide-band Spectrum Sensing
Open this publication in new window or tab >>On the use of Compressive Sampling for Wide-band Spectrum Sensing
2010 (English)In: 2010 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), IEEE , 2010, 354-359 p.Conference paper, Published paper (Refereed)
Abstract [en]

In a scenario where a cognitive radio unit wishes to transmit, it needs to know over which frequency bands it can operate. It can obtain thisknowledge by estimating the power spectral density from a Nyquist-rate sampled signal. For wide-band signals sampling at the Nyquistrate is a major challenge and may be unfeasible. In this paper we accurately detect spectrum holes in sub-Nyquist frequencies without assuming wide sense stationarity in the compressed sampled signal. A novel extension to further reduce the sub-Nyquist samples is thenpresented by introducing a memory based compressed sensing thatrelies on the spectrum to be slowly varying.

Place, publisher, year, edition, pages
IEEE, 2010
Keyword
Compressive sampling, Cognitive radio, power spectrum estimation, sub-Nyquist sampling
National Category
Signal Processing
Identifiers
urn:nbn:se:kth:diva-34412 (URN)10.1109/ISSPIT.2010.5711810 (DOI)2-s2.0-79952383891 (Scopus ID)
Conference
ISSPIT - International Symposium on Signal Processing and Information Technology, Luxor
Note

© 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. QC 20110707

Available from: 2011-07-07 Created: 2011-06-07 Last updated: 2014-08-20Bibliographically approved
2. Look Ahead Parallel Pursuit
Open this publication in new window or tab >>Look Ahead Parallel Pursuit
2011 (English)In: 2011 IEEE Swedish Communication Technologies Workshop, Swe-CTW 2011, 2011, 114-117 p.Conference paper, Published paper (Refereed)
Abstract [en]

We endeavor to improve compressed sensing reconstruction performance of parallel pursuit algorithms. In an iteration, standard parallel pursuit algorithms use a support-set expansion by a fixed number of coefficients, leading to restricted performance. To achive a better performance, we develop a look ahead strategy that adaptively chooses the best number of coefficients. We develop a new algorithm which we call look ahead parallel pursuit, where a look ahead strategy is invoked on a minimal residual norm criterion. The new algorithm provides a trade-off between performance and complexity.

Keyword
compressed sensing, greedy pursuit algorithms
National Category
Signal Processing
Identifiers
urn:nbn:se:kth:diva-60017 (URN)10.1109/Swe-CTW.2011.6082477 (DOI)2-s2.0-83755172588 (Scopus ID)978-145771878-6 (ISBN)
Conference
2011 IEEE Swedish Communication Technologies Workshop, Swe-CTW 2011. Stockholm. 19 October 2011 - 21 October 2011
Note
QC 20120113Available from: 2012-01-12 Created: 2012-01-12 Last updated: 2012-02-29Bibliographically approved
3. Look ahead orthogonal matching pursuit
Open this publication in new window or tab >>Look ahead orthogonal matching pursuit
2011 (English)In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2011, 4024-4027 p.Conference paper, Published paper (Refereed)
Series
International Conference on Acoustics Speech and Signal Processing ICASSP, ISSN 1520-6149
Keyword
Compressive sensing, sparsity, greedy algorithms
National Category
Signal Processing
Research subject
SRA - ICT
Identifiers
urn:nbn:se:kth:diva-46532 (URN)10.1109/ICASSP.2011.5947235 (DOI)000296062404133 ()2-s2.0-80051626757 (Scopus ID)
Conference
36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011; Prague; 22 May 2011 through 27 May 2011
Funder
ICT - The Next Generation
Note
QC 20111104Available from: 2011-11-03 Created: 2011-11-03 Last updated: 2012-02-29Bibliographically approved
4. Greedy pursuits of compressed sensing of jointly sparse signal
Open this publication in new window or tab >>Greedy pursuits of compressed sensing of jointly sparse signal
2011 (English)Conference paper, Published paper (Refereed)
Keyword
compressed sensing, distributed compressed sensing, joint compressed sensing
National Category
Signal Processing
Research subject
SRA - ICT
Identifiers
urn:nbn:se:kth:diva-46533 (URN)
Conference
The 2011 European Signal Processing Conference (EUSIPCO‐2011). Barcelona, Spain. August 29- September 2, 2011
Funder
ICT - The Next Generation
Note
QC 20111111Available from: 2011-11-03 Created: 2011-11-03 Last updated: 2012-02-29Bibliographically approved
5. Greedy Pursuits for Distributed Compressed Sensing
Open this publication in new window or tab >>Greedy Pursuits for Distributed Compressed Sensing
(English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476Article in journal (Other academic) Submitted
National Category
Signal Processing
Identifiers
urn:nbn:se:kth:diva-90073 (URN)
Available from: 2012-02-17 Created: 2012-02-17 Last updated: 2017-12-07Bibliographically approved

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