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Machine Learning and Intelligent Communications. 5th International Conference, MLICOM 2020, Shenzhen, China, September 26-27, 2020, Proceedings

Research Article

Assist GPS to Improve Accuracy Under Complex Road Conditions Using Sensors on Smart Phone

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  • @INPROCEEDINGS{10.1007/978-3-030-66785-6_33,
        author={Li Sheng and Rui Tian and Haibo Ye},
        title={Assist GPS to Improve Accuracy Under Complex Road Conditions Using Sensors on Smart Phone},
        proceedings={Machine Learning and Intelligent Communications. 5th International Conference, MLICOM 2020, Shenzhen, China, September 26-27, 2020, Proceedings},
        proceedings_a={MLICOM},
        year={2021},
        month={1},
        keywords={Location tracking Barometer Hidden Markov Model},
        doi={10.1007/978-3-030-66785-6_33}
    }
    
  • Li Sheng
    Rui Tian
    Haibo Ye
    Year: 2021
    Assist GPS to Improve Accuracy Under Complex Road Conditions Using Sensors on Smart Phone
    MLICOM
    Springer
    DOI: 10.1007/978-3-030-66785-6_33
Li Sheng1, Rui Tian1, Haibo Ye1,*
  • 1: College of Computer Science and Technology
*Contact email: yhb@nuaa.edu.cn

Abstract

This paper presents the design and implementation of a new vehicle tracking technology. It can assist GPS to achieve high precision under special situations. For example, the situations when the road conditions are complex or the GPS signal strength is weak. In our method, the barometer data and acceleration data are used to assist the GPS data, and the Hidden Markov Model is used to assist the location tracking. We make two key technical contributions. The first is to propose a Hidden Markov Model to combine the barometer and accelerometer reading hints for estimating the location of the vehicle. The second is to design some novel techniques for parameter estimation. The experiment shows that the accuracy of our method is improved by 19.2% compared with GPS under these special situations.

Keywords
Location tracking Barometer Hidden Markov Model
Published
2021-01-24
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-030-66785-6_33
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