SiFu: A Generic and Robust Multimodal Signal Fusion Platform for Pervasive Localization

MPhil Thesis Defence


Title: "SiFu: A Generic and Robust Multimodal Signal Fusion Platform for 
Pervasive Localization"

By

Mr. Man Yu WONG


Abstract

We consider pervasive localization where a user may sample widely 
different signal modes (GPS, WiFi, geomagnetism, BLE, etc.) and values 
over time and space. Various localization algorithms have already been 
proposed for different signal modes. To achieve higher accuracy and 
pervasive localization, these signal modes may be fused. However, previous 
works in the area are often meticulously customized for only a few (two or 
three) specific modes, and cannot support dynamic mode combination arisen 
from heterogeneous sensor sampling rates and operating conditions. Some 
other recent works assume a rather static environment or certain user 
behavior, and do not extend well to environmental changes characterized by 
missing signal values, signal noise, device heterogeneity, arbitrary phone 
carriage states, etc.

We propose SiFu, a novel, highly accurate and generic multi-modal signal 
fusion platform supporting arbitrary addition and combination of signal 
modes, and robust against operational deviations from the original design 
point. To achieve genericity, SiFu leverages upon any existing 
single-modal localization algorithms as black boxes, and unifies them into 
a multi-modal likelihood framework. It employs Bayesian deep learning to 
achieve high accuracy, and data augmentation to withstand against 
environmental variations. Using a weighted likelihood, it fuses the modes 
with inertial sensor measurements by means of a particle filter. SiFu is 
simple to implement, andis extensible to any emerging or future signals 
with only incremental training. We have conduct extensive experiments in 
three markedly different and representative sites (campus, mall and subway 
station), and show that SiFu achieves significantly higher accuracy as 
compared to other state-of-the-art approaches, cutting the localization 
error by more than 20% in our experiments. It is also robust against 
environmental variations (with 30% error reduction), even when signal 
values are greatly deviated from its original designed settings.


Date:  			Friday, 10 September 2021

Time:			10:00am - 12:00noon

Zoom meeting:
https://hkust.zoom.us/j/99250669315?pwd=dDlaWkFKUE93WXMyQVc0YTkxTkhDdz09

Committee Members:	Prof. Gary Chan (Supervisor)
 			Dr. Wei Wang (Chairperson)
 			Prof. Andrew Horner


**** ALL are Welcome ****