Abstract
Due to the received signal strength (RSS) variation, WiFi indoor positioning techniques using RSS have difficulties to provide good location estimates. To mitigate the effect of the RSS variation, this paper presents a Kalman filter-based positioning algorithm that is combined with pedestrian dead reckoning and RSS-based fingerprinting positioning. The RSS recovery and clustering methods are also introduced to enhance the accuracy of the fingerprinting positioning. Unlike other existing algorithms, the proposed algorithm estimates biases accumulated in RSS measurements based on the recursive least square estimation and removes them from the measurements. Reference points are effectively selected with clustering using the recovered RSS measurements. Hence, a more accurate location estimate can be obtained in the existence of the RSS variation. The proposed algorithm is implemented into an Android-based smartphone for test.
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