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A Novel Kalman Filter Algorithm Using Stance Detection for an Inertial Navigation System

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Communications, Signal Processing, and Systems (CSPS 2020)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 654))

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Abstract

In this paper, a modified Kalman filter with stance detection is proposed. By utilizing this stance information, the proposed Kalman filter can improve the performance of a personal inertial navigation system (INS) using microelectromechanical system (MEMS). Experiment results have confirmed that the proposed system significantly improves the performance of an INS even under harsh magnetic environment.

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Author information

Authors and Affiliations

  1. The Sino-British College, University of Shanghai for Science and Technology, 1195 Fuxing Middle Road, Xuhui District, Shanghai, China

    Zhijian Shi, Rui Lin & Gareth Peter Lewis

  2. Shanghai Jinyuan School, 2169 Xincun Road, Putuo District, Shanghai, China

    Ruochen Feng

Authors
  1. Zhijian Shi
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  2. Ruochen Feng
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  3. Rui Lin
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  4. Gareth Peter Lewis
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Corresponding author

Correspondence to Zhijian Shi .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA

    Qilian Liang

  2. Tianjin Normal University, Tianjin, China

    Wei Wang

  3. Dalian University of Technology, Dalian, China

    Xin Liu

  4. School of Information Science and Technology, Dalian Maritime University, Dalian, China

    Zhenyu Na

  5. Huazhong Agricultural University, Wuhan, China

    Xiaoxia Li

  6. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Baoju Zhang

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© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Cite this paper

Shi, Z., Feng, R., Lin, R., Lewis, G.P. (2021). A Novel Kalman Filter Algorithm Using Stance Detection for an Inertial Navigation System. In: Liang, Q., Wang, W., Liu, X., Na, Z., Li, X., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2020. Lecture Notes in Electrical Engineering, vol 654. Springer, Singapore. https://doi.org/10.1007/978-981-15-8411-4_260

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  • DOI: https://doi.org/10.1007/978-981-15-8411-4_260

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-8410-7

  • Online ISBN: 978-981-15-8411-4

  • eBook Packages: EngineeringEngineering (R0)

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