Abstract:
Network-based indoor positioning with WiFi access point (AP) RSS measurements uses channel modeling, position estimation, and error analysis. A novel sparse Bayesian learning algorithm models the radio power map (RPM) in indoor space to create an accurate channel model with RSS measurements heavily influenced by propagation attenuations, multipath reflections, and shadowing effects.
A 2-stage positioning method is developed from the RPM model. Coarse positioning begins with a room-scale indoor location. In the second stage for fine positioning, the indoor space’s RPMs are used for Bayesian location estimation.
Bayesian Cramer-Rao lower bounds verify mean squared positioning errors. The proposed RPM-based approach improves RSS-based indoor positioning by 22% with an average positioning error of 1.98 meters. More importantly, the proposed modeling and positioning method can use RSS samples’ spatial relationship to improve positioning accuracy.
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