NDLI: A Data-Driven Movement Model for Single Cellphone-Based Indoor Positioning

Content Provider	IEEE Xplore Digital Library
Author	Vathsangam, H. Tulsyan, A. Sukhatme, G.S.
Copyright Year	2011
Abstract	Indoor localization is a promising area with applications in in-home monitoring and tracking. Fingerprinting and propagation model-based WiFi localization techniques have limited spatial resolution because of grid or graph-based representations. An alternative is to incorporate dynamics models based on real-time sensing of human movement and fuse these with WiFi measurements. We present a data-driven dynamic model that tracks the inherent periodicity in walking and converts this representation into velocity. This model however is prone to drift. We correct this drift with WiFi measurements to obtain a combined position estimate. Our approach records and fuses human body movement with WiFi positioning using a single mobile phone. We characterize the movement model to obtain an estimate of error predictions. The movement model showed a best case average RMS prediction error of .25 m/s. We also present a preliminary study characterizing combined system performance across straight line and L-shaped trajectories. The framework showed lower errors across the L-shaped trajectories (mean error = 4.8 m using movement sensing versus a mean error = 6 m without movement sensing) because of the ability assess the validity of a WiFi measurement. Higher errors were observed across the straight line trajectory due to imprecise trajectories.
Starting Page	174
Ending Page	179
File Size	389980
Page Count	6
File Format	PDF
ISBN	9781457704697
DOI	10.1109/BSN.2011.33
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-05-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Legged locomotion Accelerometers IEEE 802.11 Standards Position measurement Accelerometer Probabilistic logic Data models Trajectory WiFi Indoor Localization Mobile Phone
Content Type	Text
Resource Type	Article

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