Link to the University of Pittsburgh Homepage
Link to the University Library System Homepage Link to the Contact Us Form

A Location Fingerprint Framework Towards Efficient Wireless Indoor Positioning Systems

Swangmuang, Nattapong (2009) A Location Fingerprint Framework Towards Efficient Wireless Indoor Positioning Systems. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

Primary Text

Download (1MB) | Preview


Location of mobile computers, potentially indoors, is essential information to enable locationawareapplications in wireless pervasive computing. The popularity of wireless local area networks (WLANs) inside and around buildings makes positioning systems based on readily available received signal strength (RSS) from access points (APs) desirable. The fingerprinting technique associates location-dependent characteristics such as RSS values from multiple APs to a location (namely location fingerprint) and uses these characteristics to infer the location. The collection of RSS fingerprints from different locations are stored in a database called radio map, which is later used to compare to an observed RSS sample vector for estimating the MS's location. An important challenge for the location fingerprinting is how to efficiently collect fingerprintsand construct an effective radio map for different indoor environments. In addition, analytical models to evaluate and predict "precision" performance of indoor positioning systems based on location fingerprinting are lacking. In this dissertation, we provide a location fingerprint framework that will enable a construction of efficient wireless indoor systems. We develop a new analytical model that employs a proximity graph for predicting performance of indoor positioning systems based on location fingerprinting. The model approximatesprobability distribution of error distance given a RSS location fingerprint database and its associated statistics. This model also allows a system designer to perform analysis of the internal structure of location fingerprints. The analytical model is employed to identify and eliminate unnecessary location fingerprints stored in the radio map, thereby saving on computation while performing location estimation. Using the location fingerprint properties such as clustering is also shown to help reduce computational effort and create a more scalable model. Finally, by study actual measurement with the analytical results, a useful guideline for collecting fingerprints is given.


Social Networking:
Share |


Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairKrishnamurthy, Prashantprashant@mail.sis.pitt.eduPRASHK
Committee MemberLi, Ching-Chungccl@pitt.eduCCL
Committee MemberKarimi, Hassan Ahkarimi@sis.pitt.eduHKARIMI
Committee MemberWeiss, Martinmweiss@sis.pitt.eduMBW
Committee MemberThompson, Richardthompson@mail.sis.pitt.eduRTHOMPSO
Date: 8 January 2009
Date Type: Completion
Defense Date: 11 September 2008
Approval Date: 8 January 2009
Submission Date: 13 October 2008
Access Restriction: 5 year -- Restrict access to University of Pittsburgh for a period of 5 years.
Institution: University of Pittsburgh
Schools and Programs: School of Information Sciences > Information Science
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: location fingerprint; performance model; efficient radio map; indoor position location system
Other ID:, etd-10132008-122243
Date Deposited: 10 Nov 2011 20:02
Last Modified: 15 Nov 2016 13:50


Monthly Views for the past 3 years

Plum Analytics

Actions (login required)

View Item View Item