Data Process for Indoor Positioning based on WiFi Fingerprint

Abstract

Currently, most of the existing location fingerprint indoor positioning algorithms are based on the original fingerprint database. The accuracy of the fingerprint database will directly affect the final positioning accuracy. A method based on skewness-kurtosis normality test and Kalman filter fusion is proposed in this paper. Experiments shows that the fusion algorithm can effectively remove the abrupt data and noise fluctuations for the RSSI (Received Signal Strength Indication) data, and achieve accurate and smooth output of the RSSI value.