Fusing RF and IMU Signals with HMM in a Docked Phone for Indoor Carpark Navigation

MPhil Thesis Defence


Title: "Fusing RF and IMU Signals with HMM in a Docked Phone for Indoor Carpark 
Navigation"

By

Mr. Zheng ZHANG


Abstract

Due to unavailable or weak GNSS (Global Navigation Satellite System) and 
cellular signals in an indoor carpark, we consider the challenging problem of 
navigating a driver with an offline smartphone docked at the car dashboard. 
There is some basic RF (radio-frequency) infrastructure in the premise, but due 
to signal attenuation by the car body, the location is noisy and intermittent.

Previous works on carpark navigation often require special measurement 
equipment as on-car additional infrastructure (OCAI), or perform integration of 
IMU (inertial measurement unit) signals over time.  These are either not 
cost-effective to deploy or prone to high propagation error.

We propose RICH, a novel, real-time, simple and cost-effective docked-phone 
approach to fuse RF and IMU signals for indoor carpark navigation using HMM 
(Hidden Markov Model).  RICH is the first deployment-ready learning-based 
offline fusion approach without any OCAI or error-prone IMU integration.  RICH 
uses IMU signals to classify the car speed pattern and detect its heading and 
turning.  This information and the crude RF localization are then fused in an 
HMM framework to compute the location distribution of the car.

We present an analysis of RICH complexity and present the trade-off between 
computation and accuracy.  We implement RICH in smartphones, and conduct 
extensive experiments in real carparks. As compared with the state of the art, 
RICH achieves substantially lower localization error (lower by 40%) and is 
computationally light-weight and fast (less than 10ms per location).


Date:  			Monday, 6 December 2021

Time:			10:00am - 12:00noon

Venue:			Room 3494
 			(lifts 25/26)

Committee Members:	Prof. Gary Chan (Supervisor)
 			Prof. Raymond Wong (Supervisor)
 			Prof. Albert Chung (Chairperson)
 			Dr. Dan Xu


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