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Sensors 2018, 18(3), 711; https://doi.org/10.3390/s18030711

An Adaptive Method for Switching between Pedestrian/Car Indoor Positioning Algorithms based on Multilayer Time Sequences

1,3
,
1,3,* , 2
,
1,3
and
4
1
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430072, China
2
National Network SiJiShenWang Location Service (Beijing) Co., Ltd., Beijing 102200, China
3
Collaborative Innovation Center of Geospatial Technology, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
4
Wuhan Digital Engineering Research Institute, No. 718, Luoyu Road, Hongshan District, Wuhan 430000, China
*
Author to whom correspondence should be addressed.
Received: 19 December 2017 / Revised: 22 February 2018 / Accepted: 22 February 2018 / Published: 27 February 2018
(This article belongs to the Special Issue Smartphone-based Pedestrian Localization and Navigation)

Abstract

Pedestrian dead reckoning (PDR) positioning algorithms can be used to obtain a target’s location only for movement with step features and not for driving, for which the trilateral Bluetooth indoor positioning method can be used. In this study, to obtain the precise locations of different states (pedestrian/car) using the corresponding positioning algorithms, we propose an adaptive method for switching between the PDR and car indoor positioning algorithms based on multilayer time sequences (MTSs). MTSs, which consider the behavior context, comprise two main aspects: filtering of noisy data in small-scale time sequences and using a state chain to reduce the time delay of algorithm switching in large-scale time sequences. The proposed method can be expected to realize the recognition of stationary, walking, driving, or other states; switch to the correct indoor positioning algorithm; and improve the accuracy of localization compared to using a single positioning algorithm. Our experiments show that the recognition of static, walking, driving, and other states improves by 5.5%, 45.47%, 26.23%, and 21% on average, respectively, compared with convolutional neural network (CNN) method. The time delay decreases by approximately 0.5–8.5 s for the transition between states and by approximately 24 s for the entire process. View Full-Text
Keywords: behavior context; MTS; state recognition; switching pedestrian/car positioning algorithm behavior context; MTS; state recognition; switching pedestrian/car positioning algorithm
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Gu, Z.; Guo, W.; Li, C.; Zhu, X.; Guo, T. An Adaptive Method for Switching between Pedestrian/Car Indoor Positioning Algorithms based on Multilayer Time Sequences. Sensors 2018, 18, 711.

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