Indoor User Localization
Using Mobile Devices pp^d°
Jonáš Ševčík
® Introduction
Dead Reckoning
difficult navigation in complex buildings even if equipped with signs no direct visibility to GPS satellites GPS tracking not possible
calculating position using previously determined coordinates advanced by known speed and course
position tracking - calculating steps
step length estimated using neural network [2]
course determined by gyrocompass.
Methodology
Sequential Monte Carlo Filtering
localization system based on [1, 2,3]
consists of three localization techniques
techniques merged together for more accurate results
Wi-Fi Localization
Received Signal Strength fingerprinting used to create a database of APs and their RSS mapped to location coordinates receiver's location estimated from SS maps
particles evenly spread in probable location determined by RSS fingerprint database
particles set to motion by step detection events
eliminated particles, which hypothetical motion leads through impassable obstacles
results in improvement of location estimation.
Preliminary Results
implemented prototype Android application results accurate to 2.3 meters
tP References
xa -I A. Chen et al: "An algorithm for fast model-free tracking
L J indoors", SIGMOBILE MCCR, vol. 11 no. 3, 2006
n-i S. Cho: "MEMS Based Pedestrian Navigation System", JN,
^ * vol. 59, pp. 135-153, Royal Institute of Navigation, 2006
rq-i M. Arulampalam et al: "A Tutorial on Particle Filters for
L J Online Nonlinear/Non-Gaussian Bayesian Tracking", SP, vol. 50 no. 2, 2002
european
social fund in the czech republic
EUROPEAN UNION
MINISTRY OF EDUCATION, YOUTH AND SPORTS
OP Education for Competitivness
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in
INVESTMENTS IN EDUCATION DEVELOPMENT
IMI
Acknowledgments
Thanks to all the staff members of the Masaryk University Department of Building Passportization for providing maps and consultations. Also, great thanks to Michal Holcfk and Adam Berthoty for implementing localization prototypes.