Location Estimation Through Inexact Machine Learning Approach

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dc.contributor.advisorPalem, Krishna
dc.creatorGonzalez Espana, Juan Jose
dc.date.accessioned2019-05-17T15:37:07Z
dc.date.available2019-05-17T15:37:07Z
dc.date.created2018-08
dc.date.issued2018-10-25
dc.date.submittedAugust 2018
dc.date.updated2019-05-17T15:37:07Z
dc.description.abstractLocation estimation has become a field of increasing interest in recent years. The main reason is the multiple applications that can be enabled based on this technology. Fields such as entertainment, health care, tourism and advertisement are some of the areas where a plethora of applications can be implemented. In outdoors this problem is solved, for most of the cases, with Global Navigation Systems (GNSS). However, in indoors is a current topic of interest that has been addressed from different perspectives with different technologies. Nonetheless, there is no technology that is as established as GNSS is for outdoors. One promising approach is Inertial Measurement Units (IMU) which are low cost and widely accessible in multiple SmartDevices such SmartPhones, SmartWatches, WristBands, among others. Two of the main difficulties that hinder the wide adoption of this technology are the error accumulation between estimations and the scarce availability of the Ground Truth data to train and test the models. In this work both challenges are addressed by two methods, one which corrects the error by using the structure of the map where the user is located and the other method improves the Ground Truth data provided by GNSS measurements. Energy consumption is reduced by a factor 27x when compared with GPS and the accuracy of the labels is improved by 26% on average.
dc.format.mimetypeapplication/pdf
dc.identifier.citationGonzalez Espana, Juan Jose. "Location Estimation Through Inexact Machine Learning Approach." (2018) Master’s Thesis, Rice University. <a href="https://hdl.handle.net/1911/105792">https://hdl.handle.net/1911/105792</a>.
dc.identifier.urihttps://hdl.handle.net/1911/105792
dc.language.isoeng
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.
dc.subjectLocation estimation
dc.subjectInertial Measurement Units
dc.subjectMachine Learning
dc.subjectInexactness
dc.titleLocation Estimation Through Inexact Machine Learning Approach
dc.typeThesis
dc.type.materialText
thesis.degree.departmentElectrical and Computer Engineering
thesis.degree.disciplineEngineering
thesis.degree.grantorRice University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science
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