Pedestrian Dead Reckoning Fusion Positioning Based On Radial Basis Function Neural Network

The positioning accuracy of the PDR based on the smartphone is relatively low due to the accumulative error caused by the heading in inertial navigation. In order to resolve this problem, in this paper, we use the solution that fusing the heading which is measured by gyroscope and orientation sensor. In addition, we propose a new fusion method which is realized by the radial basis function neural network and compare the fusion positioning results with the Kalman filter and Back Propagation neural network. The experimental results shows that the positioning error corresponding to 80% confidence interval processed by the radial basis function neural network is only 8.18cm, while the results of Kalman filter and Back Propagation neural network are 34 cm and 22.54 cm, respectively. The experimental results show that the proposed method has the higher positioning accuracy than the traditional Kalman filter method and Back Propagation neural network. These experimental results demonstrate that the radial basis function neural network can be used in the indoor high-precision PDR.