The positioning accuracy of the PDR based on the smartphone is relatively low due to the accumulative error caused bythe heading in inertial navigation. In order to resolve this problem, in this paper, we use the solution that fusing theheading which is measured by gyroscope and orientation sensor. In addition, we propose a new fusion method which isrealized by the radial basis function neural network and compare the fusion positioning results with the Kalman filter andBack 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 Kalmanfilter and Back Propagation neural network are 34 cm and 22.54 cm, respectively. The experimental results show that theproposed method has the higher positioning accuracy than the traditional Kalman filter method and Back Propagationneural network. These experimental results demonstrate that the radial basis function neural network can be used in theindoor high-precision PDR.
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