Design of an adaptive positioning system based on WiFi radio signals

With the remarkable advances in wireless networking and pervasive computing, there have been an increasing need to figure it into LBS (location-based services) applications. This paper proposes an adaptive positioning system for the mobile terminal (MT) localization using the radio propagation modeling (RPM) and Kalman filtering (KF) according to the measurements of signal-to-noise ratio (SNR) information between indoor IEEE 802.11b (WiFi) APs and reference points (Tags). Comparing with the conventional empirical method (such as the fingerprinting method), the adaptive RPM algorithm can perform on-line calibration in real dynamic environments and reduce the number of training points. Additionally, the KF algorithm is used to track the location of MTs, where the observation information is extracted from the empirical and RPM positioning methods. The numerical simulations and experimental results show that not only the calibrating and positioning method of the RPM algorithm can improve the accuracy of an indoor WiFi locating system and alleviate the phenomenon of aliasing in the signal space, but also the estimating and tracking method of the KF algorithm can overcome the problem of the aliasing and reduce the positioning error with smaller sampling time.