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Estimation for Sensor Fusion and Sparse Signal Processing
KTH, School of Electrical Engineering (EES), Signal Processing.
2013 (English)Doctoral thesis, monograph (Other academic)
Abstract [en]

Progressive developments in computing and sensor technologies during the past decades have enabled the formulation of increasingly advanced problems in statistical inference and signal processing. The thesis is concerned with statistical estimation methods, and is divided into three parts with focus on two different areas: sensor fusion and sparse signal processing.

The first part introduces the well-established Bayesian, Fisherian and least-squares estimation frameworks, and derives new estimators. Specifically, the Bayesian framework is applied in two different classes of estimation problems: scenarios in which (i) the signal covariances themselves are subject to uncertainties, and (ii) distance bounds are used as side information. Applications include localization, tracking and channel estimation.

The second part is concerned with the extraction of useful information from multiple sensors by exploiting their joint properties. Two sensor configurations are considered here: (i) a monocular camera and an inertial measurement unit, and (ii) an array of passive receivers. New estimators are developed with applications that include inertial navigation, source localization and multiple waveform estimation.

The third part is concerned with signals that have sparse representations. Two problems are considered: (i) spectral estimation of signals with power concentrated to a small number of frequencies,and (ii) estimation of sparse signals that are observed by few samples, including scenarios in which they are linearly underdetermined. New estimators are developed with applications that include spectral analysis, magnetic resonance imaging and array processing.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2013. , xi, 221 p.
Series
Trita-EE, ISSN 1653-5146 ; 2013:026
Keyword [en]
Estimation theory, sensor fusion, sparse signal processing
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:kth:diva-121283ISBN: 978-91-7501-716-7 (print)OAI: oai:DiVA.org:kth-121283DiVA: diva2:617972
Public defence
2013-05-17, Kollegiesalen, Brinellvägen 8, Stockholm, 13:00 (English)
Opponent
Supervisors
Note

QC 20130426

Available from: 2013-04-26 Created: 2013-04-25 Last updated: 2013-04-26Bibliographically approved

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Doktoral thesis(9629 kB)1591 downloads
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CiteExportLink to record
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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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  • de-DE
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  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
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  • Other locale
More languages
Output format
  • html
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  • asciidoc
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