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dc.contributor.advisorAl-Naffouri, Tareq Y.
dc.contributor.authorAlsaif, Muhanned
dc.date.accessioned2021-07-15T13:39:58Z
dc.date.available2021-07-15T13:39:58Z
dc.date.issued2021-07
dc.identifier.citationAlsaif, M. (2021). New Algorithms to Solve the Positioning Problem of Outdoor Localization Using Constrained and Unconstrained Optimization Techniques. KAUST Research Repository. https://doi.org/10.25781/KAUST-7UX80
dc.identifier.doi10.25781/KAUST-7UX80
dc.identifier.urihttp://hdl.handle.net/10754/670250
dc.description.abstractThe demand for outdoor precise location is increasing with the development of new applications such as autonomous vehicles, exploration robots and wireless sensor networks. Global Navigation Satellite System (GNSS) is the go-to system for outdoor localization. This thesis focuses on developing new methods for GNSS single-point positioning (SPP) model, where no access to a reference station or precise GNSS parameters is needed. We investigated the limitations of the standard method, least- squares adjustment (LSA), and we derived the Cramer-Rao bounds for the SPP estimation problem. We also investigated different techniques to formulate the positioning problem with the goal to increase the accuracy. A new method is developed by reformulating the problem as difference-of-convex program (DC program) and utilizing convex-concave procedure (CCCP) to solve the positioning problem without linearizing the observation equations. In addition, we examined the potential of multiple-receiver systems in increasing the accuracy. We formulated the multiple- receiver SPP estimation problem, and we proposed to configure the multiple receivers in a fixed equilateral triangle to exploit the symmetry and the geometrical constraints of the configuration. We extended the use of LSA in multiple-receiver system. We also developed a modification of LSA algorithm, named least-squares adjustment extension (LSAE), that utilizes attitude information and the constraints of the multiple-receiver system. In addition, we developed a new algorithm to optimizes the SPP estimates over the equilateral triangles Riemannian manifold, which enforces the geometrical constraints of the multiple-receiver system. Furthermore, we derived the constrained and the unconstrained Cramer-Rao bounds (CRB and CCRB) for the multiple-receiver SPP problem. Moreover, we investigated the influence of both attitude information and the equilateral triangle baseline length on the algorithms’ performances and the derived CCRB. Finally, we carried out a numerical analysis by implementing the algorithms and the bounds in MATLAB, where we tested the algorithms on simulated GNSS scenarios. The proposed multiple-receiver methods provide more precise estimates for the SPP problem in comparison to the single receiver methods.
dc.language.isoen
dc.subjectGNSS
dc.subjectLocalization
dc.subjectSingle-Point Positioning
dc.subjectRiemannian Optimization
dc.subjectConvex-Concave Procedure
dc.subjectMultiple-Receiver Positioning
dc.titleNew Algorithms to Solve the Positioning Problem of Outdoor Localization Using Constrained and Unconstrained Optimization Techniques
dc.typeThesis
dc.contributor.departmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
thesis.degree.grantorKing Abdullah University of Science and Technology
dc.contributor.committeememberFeron, Eric
dc.contributor.committeememberWittum, Gabriel
thesis.degree.disciplineElectrical and Computer Engineering
thesis.degree.nameMaster of Science
refterms.dateFOA2021-07-15T13:39:59Z
kaust.request.doiyes


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