An Ultra WideBand (UWB)/ Pedestrian Dead Reckoning (PDR) fusion positioning algorithm with adaptive coefficient adjustment based on UWB error prediction was proposed in order to improve the UWB performance and reduce the PDR accumulative errors in the indoor Non-Line-Of-Sight (NLOS) positioning scenes and solve the UWB performance degradation caused by environmental factors. On the basis of the creative proposal of predicting the UWB positioning errors in complex environment by Support Vector Machine (SVM) regression model, UWB/PDR fusion positioning performance was improved by adding adaptive adjusted parameters to the conventional Extended Kalman Filter (EKF) algorithm. The experimental results show that the proposed algorithm can effectively predict the current UWB positioning errors in the complex UWB environment, and increase the accuracy by adaptively adjusting the fusion parameters, which makes the positioning error reduced by 18.2% in general areas and reduced by 48.7% in the areas with poor UWB accuracy compared with those of the conventional EKF algorithm, so as to decrease the environmental impact on the UWB performance. In complex scenes of both Line-Of-Sight (LOS) and NLOS including UWB, the positioning error per 100 meters is reduced from meter scale to decimeter scale, which reduces the PDR errors in NLOS scenes.

In the process of indoor positioning and navigation, a Bayesian network-based floor localization algorithm was proposed for the problem of large error of floor localization when only the pedestrian height displacement considered. Firstly, Extended Kalman Filter (EKF) was adopted to calculate the vertical displacement of the pedestrian by fusing inertial sensor data and barometer data. Then, the acceleration integral features after error compensation was used to detect the corner when the pedestrian went upstairs or downstairs. Finally, Bayesian network was introduced to locate the pedestrian on the most likely floor based on the fusion of walking height and corner information. Experimental results show that, compared with the floor localization algorithm based on height displacement, the proposed algorithm has improved the accuracy of floor localization by 6.81%; and compared with the detection algorithm based on platform, the proposed algorithm has improved the accuracy of floor localization by 14.51%. In addition, the proposed algorithm achieves the accuracy of floor localization by 99.36% in the total 1247 times floor changing experiments.