Abstract / truncated to 115 words (read the full abstract)

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 ... toggle 7 keywords

estimation theory sensor fusion sparse signal processing bayesian greedy vision inertial navigation


Zachariah, Dave
KTH Royal Institute of Technology
Publication Year
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July 3, 2013

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