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Communications and Networking. 17th EAI International Conference, Chinacom 2022, Virtual Event, November 19-20, 2022, Proceedings

Research Article

An Improved Hidden Markov Model for Indoor Positioning

Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-34790-0_31,
        author={Xingyu Ren and Di He and Xuyu Gao and Zhicheng Zhou and Chih-Chun Ho},
        title={An Improved Hidden Markov Model for Indoor Positioning},
        proceedings={Communications and Networking. 17th EAI International Conference, Chinacom 2022, Virtual Event, November 19-20, 2022, Proceedings},
        proceedings_a={CHINACOM},
        year={2023},
        month={6},
        keywords={Indoor Positioning Hidden Markov Model IMU Machine Learning},
        doi={10.1007/978-3-031-34790-0_31}
    }
    
  • Xingyu Ren
    Di He
    Xuyu Gao
    Zhicheng Zhou
    Chih-Chun Ho
    Year: 2023
    An Improved Hidden Markov Model for Indoor Positioning
    CHINACOM
    Springer
    DOI: 10.1007/978-3-031-34790-0_31
Xingyu Ren1, Di He1,*, Xuyu Gao1, Zhicheng Zhou2, Chih-Chun Ho3
  • 1: Shanghai Key Laboratory of Navigation and Location-Based Services
  • 2: Shenzhen Dashi Intelligent Co.
  • 3: Beijing Jizhi Digital Techonology Co., Ltd., Beijing Longfor Blue Engine Industrial Park, Building 6, No.8 Beiyuan Street
*Contact email: dihe@sjtu.edu.cn

Abstract

This paper proposes an indoor positioning method combines machine learning, IMU (Inertial Measurement Unit) and an improved HMM (Hidden Markov Model). HMM is the base framework, the latent states correspond to the location grid, which uses two methods to divide the area into grids with different granularity. The fine-grained grids are used to compute transition probability, and the coarse-grained ones for emission probability. IMU data can help to adjust transition probability between fine-grained grids, and some machine learning methods to estimate the emission probability in each coarse-grained grid. And for the particularity of the model, there’s an improved Viterbi algorithm proposed to calculate the most likely path, which is a more robust version compared with the original one.

Keywords
Indoor Positioning Hidden Markov Model IMU Machine Learning
Published
2023-06-10
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-34790-0_31
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