Hidden Markov Model-based Pedestrian Navigation System using MEMS Inertial Sensors

Zhang, Yingjun; Liu, Wen; Yang, Xuefeng; Xing, Shengwei

doi:10.1515/msr-2015-0006

Abstract

In this paper, a foot-mounted pedestrian navigation system using MEMS inertial sensors is implemented, where the zero-velocity detection is abstracted into a hidden Markov model with 4 states and 15 observations. Moreover, an observations extraction algorithm has been developed to extract observations from sensor outputs; sample sets are used to train and optimize the model parameters by the Baum-Welch algorithm. Finally, a navigation system is developed, and the performance of the pedestrian navigation system is evaluated using indoor and outdoor field tests, and the results show that position error is less than 3% of total distance travelled.

References

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Hidden Markov Model-based Pedestrian Navigation System using MEMS Inertial Sensors

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