RF-Based Localization For People Sensing: approaches and comparisons

PhD Qualifying Examination


Title: "RF-Based Localization For People Sensing: approaches and comparisons"

by

Mr. Jiajie TAN


Abstract:

With the penetration of Internet of things (IoT) and advances in smart sensing 
technologies, there has been increasing interest in people sensing. 
Localization, as an important concern, acts as the cornerstone of many 
people-sensing applications such as crowd monitoring and elderly care. Among 
various techniques, the RF-based approach has emerged as the promising one 
because of its relatively large sensing range, high penetration, flexible 
deployment, low cost, etc. In this survey, we review over classical and recent 
RF-based localization techniques for people sensing applications. We consider 
using pre-installed sensors to monitor signals for location determination. 
Particularly, schemes can be categorized into two directions: device-based and 
device-free based on whether RF-enabled devices are attached to subjects. The 
device-based category assumes target people to carry beaconing devices like 
mobile phones. Locations are determined according to the measurement on the 
signals sent from attached devices. Techniques based on distance, bearing and 
fingerprinting are widely used to tackle the problem. While in the device-free 
case, there is no attached device required on the subjects. Instead, the 
influence of appearing people on signal links is considered for localization 
and tracking. This survey reviews both fundamentals and recent advances for 
each category. We also study and compare their strengths and weaknesses under 
practical deployment. Our survey can serve as an educating study for new 
researchers into people-sensing application as well as RF-based localization.


Date:			Friday, 3 August 2018

Time:                  	10:00am - 12:00noon

Venue:                  Room 3494
                         Lifts 25/26

Committee Members:	Prof. Gary Chan (Supervisor)
 			Prof. James Kwok (Chairperson)
 			Prof. Andrew Horner
 			Dr. Xiaojuan Ma


**** ALL are Welcome ****