Date of Award
2016
Publication Type
Master Thesis
Degree Name
M.A.Sc.
Department
Electrical and Computer Engineering
Supervisor
Tepe, Kemal
Rights
info:eu-repo/semantics/openAccess
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Abstract
Technology advancements such as GPS, automation and robotics have completely changed the world and produced new industries, once thought to be unimaginable a century ago. As with all technology, these systems come with limitations and can be further improved. At this time, all of these systems share one common problem; they cannot work together in an indoor environment. The advent of indoor positioning systems aims to create a union between these technologies such as allowing robots to be location aware. Indoor positioning is currently a new technology with no defined standard. Ultra-wideband based indoor positioning systems have become popular because of their resistance to multipath and high resolution due to a large bandwidth. The Ultra-wideband based system in this thesis utilizes the time of arrival technique to calculate distances and thus a user’s position. Time of arrival is only reliable when there is a line-of-sight between two transceivers. If there is no line-of-sight, the distances calculated are inaccurate thus impacting the accuracy of a user’s position. This thesis proposes a practical, non-hardware intensive solution to identify if there is a no line-of-sight condition and mitigates the measured range between a tag and the anchor nodes. Line-of-sight identification was implemented using the channel impulse response data. Ranging and positioning mitigation was achieved using a geometric based mitigation scheme. An accuracy of 90% was achieved for the identification of no line-of-sight and an improvement factor of 2.81 was achieved for the calculated mitigated position of a tag.
Recommended Citation
Mati, Michal, "Identification & Mitigation of NLOS Information for UWB based Indoor Localization" (2016). Electronic Theses and Dissertations. 5847.
https://scholar.uwindsor.ca/etd/5847
