Pedestrian dead reckoning fusion positioning based on radial basis function neural network

Haiqi Zhang; Lihui Feng; Chen Qian

doi:10.1117/12.2556322

12 March 2020 Pedestrian dead reckoning fusion positioning based on radial basis function neural network

Haiqi Zhang, Lihui Feng, Chen Qian

Proceedings Volume 11438, 2019 International Conference on Optical Instruments and Technology: Optoelectronic Imaging/Spectroscopy and Signal Processing Technology; 1143817 (2020) https://doi.org/10.1117/12.2556322
Event: 2019 International Conference on Optical Instruments and Technology, 2019, Beijing, China

Abstract

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.

Citation Download Citation

Haiqi Zhang, Lihui Feng, and Chen Qian "Pedestrian dead reckoning fusion positioning based on radial basis function neural network", Proc. SPIE 11438, 2019 International Conference on Optical Instruments and Technology: Optoelectronic Imaging/Spectroscopy and Signal Processing Technology, 1143817 (12 March 2020); https://doi.org/10.1117/12.2556322

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