An accurate and robust indoor localization system

Pao Yue-kong Library Electronic Theses Database

An accurate and robust indoor localization system


Author: Chan, Chun-lun Eddie
Title: An accurate and robust indoor localization system
Degree: Ph.D.
Year: 2010
Subject: Hong Kong Polytechnic University -- Dissertations
Robust control
Localization theory
Wireless LANs
Department: Dept. of Computing
Pages: xx, 173 p. : ill. (some col.) ; 30 cm.
InnoPac Record:
Abstract: A wireless tracking system such as the Global Positioning System (GPS) is the most effective in relatively open and flat outdoor environments but is much less effective in non-line-of-sight (NLOS) environments such as hilly, mountainous, or built-up areas. In recent years, IEEE 802.11 Wireless Local Area Networks (WLANs) have been widely deployed and are now fuelling a wide range of location-aware computing applications. The localization of devices in these networks (i.e., estimating their location) makes use of the Wi-Fi signal strength. There are two location-sensing techniques for indoor environments, propagation-based and location-fingerprinting techniques. Although these two techniques can locate WLAN-enabled devices, recent research on indoor localization system is still not satisfactory. There are five major problems that lead to inaccurate and low efficiency localization systems. First, signal overlaps and interferences seriously worsen the performance of localization system. Second, existing positioning algorithms suffer from high computational complexity of location estimation as a result of the burden of having to carry out many signal sampling, weighting, and filtering tasks. Third, existing WLAN infrastructures are often deployed in an ad-hoc, empirical and non-optimal configurations. Such unstructured approaches lead to poor resource utilization and inaccurate localization due to signal overlap. Fourth, there is a lack of analytical models that can be used as a framework for designing and deploying the positioning systems. Most existing models ignore radio signal properties and some assume the distribution of the RSS is Gaussian and pair wise. Such assumptions may ignore or distort the real behavior of RSS and lead to RSS analysis that is not accurate enough. Finally, a localization system should provide location-aware information according to the user{174}s needs. However, it is very difficult to retrieve location-aware information that matches the behavior of users and at the same time suits current conditions. An accurate, robust, scalable and cost-effective localization solution should have (1) a stable WLAN signal transmission, (2) an accurate positioning algorithm, (3) a structural WLAN infrastructure that can support intensive tracking, (4) a model that can visualize the WLAN signal distribution to prevent the occurrence of signal black spots and interference and finally (5) a query interpretation and information retrieval system that can provide location-aware information suitable to user behaviors and preferences.
This thesis proposes an indoor localization framework that tackles each of these problems. First, it proposes a cell-based installation of wireless access points and suggests a channel assignment scheme to have a stable WLAN signal transmission. Second, it makes use of the Newton Trust-Region algorithm and Kalman Filter to improve the accuracy of positioning algorithms. Third, it suggests the use of Fuzzy Logic and Topographic modeling to visualize the signal distribution in a 2-D and 3-D manner. Finally, it develops an agent-based module for retrieving location-aware information that can improve the speed of retrieval while maintaining or even improving the accuracy by making use of semantic information in the data to develop smaller training sets. A series of experiments was carried out to measure the performance of the proposed framework and contrast the result with existing approaches. The major findings are that the proposed framework could help engineers to save 50% of WLAN infrastructure resources (access points) while at the same time increasing the accuracy of localization by 20%. Experimental analysis also shows that channel interference usually obeys a right-skewed distribution and positioning accuracy is greatly affected by channel interference between access points. Finally, the proposed framework provides a quick reference and efficient analytical tool for improving the design of WLAN infrastructure.

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