INTELLIGENT ALGORITHMS FOR HIGH ACCURACY INDOOR POSITIONING AND TRACKING

The capacity to navigate and identify individuals and other devices is becoming increasingly common and more essential in the era of the Internet of Things and the blooming of Wireless Sensor Networks. Outdoor positioning has been shown very well and is commonly used in everyday life thanks to the well-known GPS scheme. However, owing to the unique problems and distinctive requirements in the indoor environment, indoor positioning is still undergrowth and has attracted a lot of research and development in recent times. It can be said that finding an extensive solution like GPS in outdoor positioning will be nearly impossible. We need to evaluate the demands of the application and system so that we can determine the appropriate technology for the navigation system. Not only diversified in demands and technology, but the range of appliances engaged in the system also affects indoor positioning. This heterogeneity covers different kinds of operating systems and communication protocols. Besides, the problems recognised for indoor conditions such as the fading impact, signal attenuation, signal blocking, noise, and interference still cause the navigation system many problems. As a prospective technology candidate for indoor navigation devices, the Bluetooth Low Energy and iBeacon have appeared. The outstanding properties of Bluetooth Low Energy such as low consumption of energy, simplicity and elevated market penetration draw the attention of scientists. In this thesis, I develop an indoor positioning system using Bluetooth Low Energy technology. My scheme is based on a range-based technique that requires knowledge of beacons before positioning. Users with Bluetooth-enabled devices situated in the system region can be positioned by gathering RSSI signals. Then the information gathered will be filtered and processed through the proposed algorithm. Experiments demonstrate that my system achieves auspicious results with the error margin under half a metre for static devices. In addition, a mobile device sensor is used to measure inertial information. Applying pedestrian dead reckoning technique, direction and target position are estimated after that. Combining this outcome with my algorithms, the tracking results of my system achieved an error of about 0.2m.

Name dc:type.qualificationname

phd

Level dc:type.qualificationlevel

doctoral

Grantor dc:publisher.institution

Liverpool John Moores University

Year dc:date.issued

2020

Author dc:creator

• Nguyen, Q

Contributors dc:contributor

• Johnson, P
• Nguyen, TT
• Randles, M

• T Technology (General)
• TA Engineering (General). Civil engineering (General)
• TK Electrical engineering. Electronics. Nuclear engineering

DOI dc:identifier.doi

https://doi.org/10.24377/LJMU.t.00012935

OAI identifier oai:identifier

oai:researchonline.ljmu.ac.uk:12935