Dynamic online calibrated radio maps for indoor positioning in wireless local area networks

1. Dynamic Online-Calibrated Radio Maps for Indoor Positioning In Wireless
Local Area Networks
Context-awareness and Location-Based-Services are of great importance in mobile computing
environments. Although fingerprinting provides accurate indoor positioning in Wireless Local
Area Networks (WLAN), difficulty of offline site surveys and the dynamic environment changes
prevent it from being practically implemented and commercially adopted. This paper introduces
a novel client/server-based system that dynamically estimates and continuously calibrates a fine
radio map for indoor positioning without extra network hardware or prior knowledge about the
area and without time-consuming offline surveys. A modified Bayesian regression algorithm is
introduced to estimate a posterior signal strength probability distribution over all locations based
on online observations from WLAN access points (AP) assuming Gaussian prior centered over a
logarithmic pass loss mean. To continuously adapt to dynamic changes, Bayesian kernels
parameters are continuously updated and optimized genetically based on recent APs
observations. The radio map is further optimized by a fast features reduction algorithm to select
the most informative APs. Additionally, the system provides reliable integreity monitor
(accuracy measure). Two different experiments on IEEE 802.11 networks show that the dynamic
radio map provides 2-3m accuracy which is comparable to results of an up-to-date offline radio
map. Also results show the consistency of estimated accuracy measure with positioning
accuracy.
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