Fine-grained indoor positioning and tracking systems

Li, Zan (2016). Fine-grained indoor positioning and tracking systems. (Dissertation, Universität Bern, Philosophisch-naturwissenschaftliche Fakultät, Institut für Informatik)

Preview

Text 16li_z.pdf - Published Version Available under License Creative Commons: Attribution-Noncommercial-No Derivative Works (CC-BY-NC-ND). Download (7MB) | Preview

Indoor positioning has attracted considerable attention for decades due to the increasing
demands for location based services. In the past years, although numerous
methods have been proposed for indoor positioning, it is still challenging to find a
convincing solution that combines high positioning accuracy and ease of deployment.
Radio-based indoor positioning has emerged as a dominant method due to
its ubiquitousness, especially for WiFi. RSSI (Received Signal Strength Indicator)
has been investigated in the area of indoor positioning for decades. However, it
is prone to multipath propagation and hence fingerprinting has become the most
commonly used method for indoor positioning using RSSI. The drawback of fingerprinting
is that it requires intensive labour efforts to calibrate the radio map
prior to experiments, which makes the deployment of the positioning system very
time consuming. Using time information as another way for radio-based indoor
positioning is challenged by time synchronization among anchor nodes and timestamp
accuracy. Besides radio-based positioning methods, intensive research has
been conducted to make use of inertial sensors for indoor tracking due to the fast
developments of smartphones. However, these methods are normally prone to accumulative
errors and might not be available for some applications, such as passive
positioning.
This thesis focuses on network-based indoor positioning and tracking systems,
mainly for passive positioning, which does not require the participation of targets
in the positioning process. To achieve high positioning accuracy, we work on some
information of radio signals from physical-layer processing, such as timestamps
and channel information. The contributions in this thesis can be divided into two
parts: time-based positioning and channel information based positioning. First,
for time-based indoor positioning (especially for narrow-band signals), we address
challenges for compensating synchronization offsets among anchor nodes, designing
timestamps with high resolution, and developing accurate positioning methods.
Second, we work on range-based positioning methods with channel information to
passively locate and track WiFi targets. Targeting less efforts for deployment, we
work on range-based methods, which require much less calibration efforts than fingerprinting.
By designing some novel enhanced methods for both ranging and positioning
(including trilateration for stationary targets and particle filter for mobile
targets), we are able to locate WiFi targets with high accuracy solely relying on radio
signals and our proposed enhanced particle filter significantly outperforms the
other commonly used range-based positioning algorithms, e.g., a traditional particle
filter, extended Kalman filter and trilateration algorithms. In addition to using
radio signals for passive positioning, we propose a second enhanced particle filter
for active positioning to fuse inertial sensor and channel information to track indoor
targets, which achieves higher tracking accuracy than tracking methods solely
relying on either radio signals or inertial sensors.

Interest & Impact

Downloads

709 since deposited on 18 Apr 2016
55 in the past 12 months

Citations

5 Citations in Web of Science ®
6 Citations in Scopus

Search

in Google Scholar™

Services

Actions (login required)

Edit item

Actions (login required)

Edit item