NDLI: Knowledge-based error detection and correction method of a Multi-sensor Multi-network positioning platform for pedestrian indoor navigation

Content Provider	IEEE Xplore Digital Library
Author	Yuwei Chen Ruizhi Chen Ling Pei Kroger, T. Kuusniemi, H. Jingbin Liu Wei Chen
Copyright Year	2010
Description	Author affiliation: Department of Electronic Science and Technology, University of Science and Technology of China (USTC), Hefei, China (Wei Chen) \|\| Department of Navigation and Positioning, Finnish Geodetic Institute, Geodeetinrinne 2, Masala, Finland, 02430 (Yuwei Chen; Ruizhi Chen; Ling Pei; Kroger, T.; Kuusniemi, H.; Jingbin Liu)
Abstract	For pedestrian indoor navigation, an accurate 2D/3D position is a premise for any further processing. Currently, indoor navigation is a challenging task for standalone GNSS technology. FGI has integrated self-contained sensors with wireless locating technology to investigate a hybrid indoor positioning solution. However, the infrastructure indoors inflicts multiple disturbances to the positioning sensors. A knowledge-based error detection and correction method is applied to detect and eliminate the occurring gross errors. Six modes of user dynamics are extracted from measurements of a barometer and an accelerometer, and such contexts can improve the positioning accuracy and enhance the user experience of the final navigation application.
Starting Page	873
Ending Page	879
File Size	540898
Page Count	7
File Format	PDF
ISBN	9781424450367
ISSN	21533598
DOI	10.1109/PLANS.2010.5507190
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-05-04
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Error correction Satellite navigation systems Pollution measurement Wireless sensor networks Accelerometers Indoor environments Global Positioning System Bluetooth Wireless LAN Fingerprint recognition Knowledge-based Pedestrian Indoor navigation Error Detection
Content Type	Text
Resource Type	Article

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