TOWARDS UBIQUITOUS INDOOR LOCALIZATION SERVICE VIA MULTI-MODAL SENSING ON SMARTPHONES

PhD Thesis Proposal Defence


Title: "TOWARDS UBIQUITOUS INDOOR LOCALIZATION SERVICE VIA MULTI-MODAL 
SENSING ON SMARTPHONES"

by

Mr. Han XU


Abstract:

Indoor localization is of great importance to a wide range of applications 
in this era of mobile computing, attracting extensive research effort over 
recent decades. Current mainstream solutions rely on Received Signal 
Strength (RSS) of wireless signals as fingerprints to distinguish and 
infer locations. However, those methods suffer from fingerprint ambiguity 
that roots in multipath fading and temporal dynamics of wireless signals, 
which invalidate theoretical propagation models, distort received signal 
signatures, and fundamentally constrain the performance of indoor 
localization. With the trend moving towards equipment of smart devices in 
daily life and adoption of enhanced sensors, we identify the opportunity 
of ubiquitous indoor localization service via the multi-modal sensing 
abilities on smartphones. In the first work, we propose Argus, an 
image-assisted localization system for mobile devices by harnessing their 
Visual Sensing abilities. The basic idea of Argus is to extract geometric 
constraints from crowdsourced photos, and to reduce fingerprint ambiguity 
by mapping the constraints jointly against the fingerprint space. In the 
second work, we design TUM, an Acoustic Sensing localization scheme 
Towards Ubiquitous Multi-device localization. The basic idea of TUM is to 
utilize the dual-microphones and speakers to obtain distance cues among 
devices, while resolving the localization ambiguity with the help of MEMS 
sensors. In the third work, we exploit the Inertial Sensing abilities on 
smartphones and propose RAD. The basic idea is to automatically generate a 
fingerprint database through space partition, of which each cell is 
fingerprinted by its maximum influence APs. Based on this robust location 
indicator, fine-grained localization can be achieved by a discretized 
particle filter utilizing sensor data fusion. We prototype the above three 
schemes with commodity devices, and evaluate their performances in various 
indoor environments. Experimental results demonstrate improved indoor 
localization accuracy, better user interaction and less overhead compared 
with classical RSS-based schemes.


Date:			Thursday, 2 June 2016

Time:                  	1:00pm - 3:00pm

Venue:                  Room 5506
                         (lifts 25/26)

Committee Members:	Dr. Ke Yi (Supervisor)
  			Dr. Qiong Luo (Chairperson)
 			Prof. Gary Chan
  			Dr. Lei Chen


**** ALL are Welcome ****