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Greedy Pursuits for Distributed Compressed Sensing
KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0003-2638-6047
KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-7926-5081
(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: urn:nbn:se:kth:diva-90073OAI: oai:DiVA.org:kth-90073DiVA: diva2:504059
Available from: 2012-02-17 Created: 2012-02-17 Last updated: 2017-12-07Bibliographically approved
In thesis
1. Compressed Sensing: Algorithms and Applications
Open this publication in new window or tab >>Compressed Sensing: Algorithms and Applications
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
compressed sensing, greedy pursuits, subspace pursuit, orthogonal matching pursuit, power spectral density estimation, distributed compressed sensing
National Category
Telecommunications
Identifiers
urn:nbn:se:kth:diva-90074 (URN)978-91-7501-269-8 (ISBN)
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

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Chatterjee, SaikatSkoglund, Mikael

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