Abstract:Aiming at the low accuracy problem in the data fusion of indoor pedestrian positioning and navigation using traditional Kalman filter algorithm, this paper proposes a new pedestrian dead reckoning navigation algorithm using rank Kalman filter (RKF) based on pedestrian dead reckoning technology. RKF technology can nicely handle nonGaussian and nonlinear system due to its special rank sampling mechanism. Through combining RKF technology and zero velocity update(ZUPT) technology, the algorithm can fuse the multisensor data measured in indoor pedestrian motion, and achieve more accurate indoor pedestrian positioning and navigation. Firstly, the zero velocity detection algorithm is used to analyze and obtain the zero velocity information from the data measured with MEMS sensors. Then, the obtained zero velocity information is used as ZUPT, which is fused with the information from the RKF algorithm and the pedestrian position is obtained. Finally, the experiment result shows that indoor pedestrian dead reckoning(PDR) algorithm based on RKF achieves certain improvement compared with the pedestrian dead reckoning algorithm using extended Kalman filter, and reduces the indoor pedestrian navigation and positioning error by about 1891%. .txt