Research on Indoor Positioning Based on Fusion of WI-FI/PDR

Abstract

Due to variance problems of the Received Signal Strength Indicator (RSSI), the Wi-Fi fingerprinting (WF) positioning has great volatility. Although several calibration algorithms have been proposed, the effect is unsatisfied. In this paper, we presented a new algorithm which combines WF and Precision Depth Recorder (PDR) based on Unscented Kalman Filter (UKF) to optimize the positioning results. Moreover, an adaptive-step-length-estimation (ASLE) algorithm, which can gain good performance while tracking the pedestrians, was designed to reduce the cumulative error of PDR positioning system. Experiments show that all error distances are less than 4.46m, of which 79% are within 2m. The combination of WF and PDR leads to a more stable indoor positioning over the long term.