NDLI: Reducing the Calibration Effort for Probabilistic Indoor Location Estimation

Content Provider	IEEE Xplore Digital Library
Author	Xiaoyong Chai Qiang Yang
Copyright Year	2002
Abstract	WLAN location estimation based on 802.11 signal strength is becoming increasingly prevalent in today's pervasive computing applications. Among the well-established location determination approaches, probabilistic techniques show good performance and, thus, become increasingly popular. For these techniques to achieve a high level of accuracy, however, a large number of training samples are usually required for calibration, which incurs a great amount of offline manual effort. In this paper, we aim to solve the problem by reducing both the sampling time and the number of locations sampled in constructing a radio map. We propose a novel learning algorithm that builds location-estimation systems based on a small fraction of the calibration data that traditional techniques require and a collection of user traces that can be cheaply obtained. When the number of sampled locations is reduced, an interpolation method is developed to effectively patch a radio map. Extensive experiments show that our proposed methods are effective in reducing the calibration effort. In particular, unlabeled user traces can be used to compensate for the effects of reducing the calibration effort and can even improve the system performance. Consequently, manual effort can be reduced substantially while a high level of accuracy is still achieved
Sponsorship	IEEE Computer Society
File Size	3056199
File Format	PDF
ISSN	15361233
Volume Number	6
Issue Number	6
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-06-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Calibration Sampling methods Interpolation Wireless LAN Phase estimation Pervasive computing Manuals System performance Bayesian methods Hidden Markov models EM. Location estimation 802.11 signal strength interpolation Hidden Markov Model
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications Electrical and Electronic Engineering Software

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