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Identifier 000386805
Title Location sensing via sparse and low rank signal models
Alternative Title Εκτίμηση θέσης με τεχνικές αραιών αναπαραστάσεων και μοντέλα σημάτων χαμηλής τάξης
Author Νικητάκη, Σοφία
Thesis advisor Τσακαλίδης, Παναγιώτης
Reviewer Τραγανίτης, Απόστολος
Παπαδοπούλη, Μαρία
Abstract Location and mobility management are major functions and essential features for seamless and ubiquitous environments. Self-organizing sensor networks, health care monitoring, personal tracking and context dependent information services are some of the potential applications. Received signal strength (RSS) fingerprinting is a highly accurate location technique that has the major advantage of exploiting already existing infrastructure to avoid additional deployment costs. Fingerprint based localization systems adopt a calibration phase in order to create signature maps that represent the physical space by capturing the variations of the dynamic nature of indoor propagation. These maps, or fingerprints, are compared to the RSS at the runtime phase in order to perform localization. This thesis explores the notion of sparsity and reformulates the problem of user localization as a sparse approximation problem. The proposed fingerprint-based localization techniques adopt the Compressed Sensing (CS) framework, which provides a new paradigm for recovering signals being sparse in some basis by means of a limited amount of randomly received measurements. Specifically, exploiting the observation that the base stations receive correlated signals from the mobile devices, we propose two CS-based algorithms: a centralized and a decentralized one. According to the centralized Jointly CS scheme all local runtime measurements received from the mobile device are sent to a central unit to perform location estimation. On the contrary, the decentralized scheme builds upon gossip consensus based approaches to distribute decision estimations in the network. Although fingerprint CS-based systems achieve high accuracy, issues that concern both the calibration and the location estimation phases can potentially limit the accuracy and scalability of these systems. Concerning the automation of the calibration phase required by fingerprint based systems, we propose a wireless localization and laser-scanner assisted Fingerprinting system that provides autonomous signature map generation. During the location estimation phase, the system mitigates the existing problems adopting a Bayesian formalism that incorporates a sparsity prior and dynamically determines the sufficient number of runtime measurements required for accurate positioning. Further challenges related to typical fingerprint-based schemes arise since it is implicitly assumed that communication occurs over the same frequency channel during the training and the runtime phases. When this assumption is violated, the mismatches between training and runtime fingerprints can significantly deteriorate the localization performance. Additionally, the exhaustive calibration procedure required during training limits the scalability of this class of methods, especially in the case where no additional hardware is utilized. To address these limitations, we propose a novel fingerprint collection technique without the need of additional hardware that significantly reduces the calibration time by pseudorandom channel sampling. The sub-sampled signature map is reconstructed as an instance of the Matrix Completion problem. Finally, we propose a reduced effort recalibration technique for fingerprint-based indoor positioning systems. The proposed method exploits the dynamic characteristics of an indoor environment and considers that a sub-set of measurements may explicitly depend on past measurements. Particularly, we minimize the number of RSS fingerprints by performing pseudo-random sub-sampling in space. The proposed framework exploits the spatial correlation structure of the RSS fingerprints while considering prior information provided from previously observed measurements, to reconstruct the signature map.
Language English
Subject Compressed sensing
Indoor localization
Matrix completion
Spatial sparsity
Αραιότητα στο χώρο
Συμπιεσμένη δειγματοληψία
Συμπλήρωση πίνακα
Issue date 2014-07-08
Collection   Faculty/Department--Faculty of Sciences and Engineering--Department of Computer Science--Doctoral theses
  Type of Work--Doctoral theses
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