Development and Optimisation of Wireless Indoor localisation for the IoT Solutions

Yu, Zheng Yu

Development and Optimisation of Wireless Indoor localisation for the IoT Solutions

Yu, Zheng Yu

Permalink

Publication Type:: Thesis
Issue Date:: 2020

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download contents and abstractAdobe PDF (537.75 kB)

Adobe PDF

Download thesisAdobe PDF (9.57 MB)

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Yu, Zheng Yu
dc.date.accessioned	2020-08-31T03:15:17Z
dc.date.available	2020-08-31T03:15:17Z
dc.date.issued	2020
dc.identifier.uri	http://hdl.handle.net/10453/142428
dc.description	University of Technology Sydney. Faculty of Engineering and Information Technology.	en_AU
dc.description.abstract	Traditional indoor localisation technologies are based on beacon technology, ultrasonics, laser range localisation, or Ultra-Wide Band (UWB) system, and others. Recently, some of these localisation techniques are used in the industry by developers of iBeacon systems for finding the position of an object with Bluetooth sensors. There are various advantages of using the iBeacon-like systems, such as low-cost, a simple signalling process, and the ease of set-up and maintenance. However, using the iBeacon-based system is marked with poor accuracy. With current technology, it is difficult to obtain highly accurate localisation for indoor objects or to perform their tracking. Also, iBeacons are highly susceptible to environmental noise interference and other radio signals. To solve these issues, this research work involves investigation and development of the error modelling algorithms that can calibrate the signal sensors, reduce the errors, mitigate noise levels and interference signals. This thesis presents a new family of error modelling algorithms based on the Curve Fitted Kalman Filter (CFKF) technique. As a part of the research investigation, a range of experiments were executed to validate the accuracy, reliability and viability of the CFKF approach. Experimental results indicate that this novel approach significantly improves the accuracy and precision of beacon-based localisation. Validation tests also show that the CFKF error modelling method can improve the localisation accuracy of UWB-based solutions.	en_AU
dc.format	Thesis (PhD)
dc.language.iso	en_US	en_US
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/142428/2/02whole.pdf
dc.rights	au.edu.uts.lib/ppc
dc.rights	The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Development and Optimisation of Wireless Indoor localisation for the IoT Solutions	en_AU
dc.type	Thesis
utslib.copyright.status	open_access	*

Abstract:

Traditional indoor localisation technologies are based on beacon technology, ultrasonics, laser range localisation, or Ultra-Wide Band (UWB) system, and others. Recently, some of these localisation techniques are used in the industry by developers of iBeacon systems for finding the position of an object with Bluetooth sensors. There are various advantages of using the iBeacon-like systems, such as low-cost, a simple signalling process, and the ease of set-up and maintenance. However, using the iBeacon-based system is marked with poor accuracy. With current technology, it is difficult to obtain highly accurate localisation for indoor objects or to perform their tracking. Also, iBeacons are highly susceptible to environmental noise interference and other radio signals. To solve these issues, this research work involves investigation and development of the error modelling algorithms that can calibrate the signal sensors, reduce the errors, mitigate noise levels and interference signals. This thesis presents a new family of error modelling algorithms based on the Curve Fitted Kalman Filter (CFKF) technique. As a part of the research investigation, a range of experiments were executed to validate the accuracy, reliability and viability of the CFKF approach. Experimental results indicate that this novel approach significantly improves the accuracy and precision of beacon-based localisation. Validation tests also show that the CFKF error modelling method can improve the localisation accuracy of UWB-based solutions.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/142428