Particle Filter for Indoor Map-Aided Navigation: Trade Off between Estimation Accuracy and Computational Speed

Chunyang Yu, Haiyu Lan, Qifan Zhou, Naser El-Sheimy

Peer Reviewed

Abstract:	This paper uses a lower priced map-aided pedestrian navigation method, integrating free indoor map information and low-cost MEMS-based inertial sensors. The map information is used as a probability distribution constraint to correct the INS-derived navigation solutions using the particle filter (PF) method. Different kinds of PFs, such as traditional sequential importance resampling (SIR) PF, Auxiliary PF (APF), and Backtracking PF (BPF) are implemented in this research to find an accelerated particle filter for both speed and accuracy. Moreover, to improve the estimation speed of PF, a two-layer pedestrian navigation system is presented to reduce the computational burden of PF by cutting down its update frequency. Real test experiments are conducted in this cascade connected integration system to evaluate the performance of the proposed two-layer Pedestrian Navigation system, and help the user to find a balance between the computational speed and the accuracy, and then select one suitable PF in our pedestrian navigation system.
Published in:	Proceedings of the 2016 International Technical Meeting of The Institute of Navigation January 25 - 28, 2016 Hyatt Regency Monterey Monterey, California
Pages:	683 - 688
Cite this article:	Yu, Chunyang, Lan, Haiyu, Zhou, Qifan, El-Sheimy, Naser, "Particle Filter for Indoor Map-Aided Navigation: Trade Off between Estimation Accuracy and Computational Speed," Proceedings of the 2016 International Technical Meeting of The Institute of Navigation, Monterey, California, January 2016, pp. 683-688. https://doi.org/10.33012/2016.13465
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