Abstract
Pedestrian navigation in a non beacon environment is a difficult problem and a hot research topic in the field of navigation. This paper introduces a design which implement the pedestrian dead reckoning based on the characteristics of human gait,and the MIMU is fixed on the waist behind. The traditional step-length model can’t adapt to all kinds of moving forms, so a new self-learning step-length estimated model is proposed by the combination of SINS and PDR. The speed is introduced from SINS to estimate the step-length at the beginning. And the step-length is used to train the step-length model based on frequency and acceleration variance. After a period of time, the speed begin to divergence and we use the trained model to estimate the step-length. Tests have been done in the indoor and outdoor. The test results show that the TTD error is less than 2 %,and most of results are around 1 %.
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© 2016 Springer Science+Business Media Singapore
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Zhao, H., Li, Q. (2016). Research on Step-Length Self-learning Pedestrian Self-location System. In: Zhang, L., Song, X., Wu, Y. (eds) Theory, Methodology, Tools and Applications for Modeling and Simulation of Complex Systems. AsiaSim SCS AutumnSim 2016 2016. Communications in Computer and Information Science, vol 646. Springer, Singapore. https://doi.org/10.1007/978-981-10-2672-0_25
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DOI: https://doi.org/10.1007/978-981-10-2672-0_25
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