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Indoor Positioning with Sensors in a Smartphone and a Fabricated High-Precision Gyroscope

  • Dianzhong ChenEmail author
  • Wenbin Zhang
  • Zhongzhao Zhang
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 516)

Abstract

In the paper, an indoor positioning scheme combining pedestrian dead reckoning (PDR) and magnetic strength matching (MSM) is proposed. PDR is conducted by sensing acceleration and angular speed through the 3-axis accelerometer in iphone7 and a fabricated high-precision rotational gyroscope. Low bias stability (0.5°/h) of the gyroscope contributes to a small accumulative error in heading angle estimation. Through data analysis to outputs of the accelerometer and the gyroscope, human motion, such as walking a step, walking upstairs or downstairs, turning left or right, is recognized and walking path is reckoned with motion information. Magnetic strength is measured by the magnetometer in iphone7 and MSM positioning result is used to reduce error of reckoned heading angle. The error rate of downstairs/upstairs step count is low and after heading angle correction by MSM, a satisfactory indoor positioning result is obtained.

Keywords

Indoor positioning Pedestrian dead reckoning (PDR) Magnetic strength matching (MSM) Modified dynamic time warping (DTW) algorithm 

References

  1. 1.
    He Z. High-sensitivity GNSS Doppler and velocity estimation for indoor navigation. Engineering Engineering–AerospaceEngineering–Electronics and Electrical, 2013.Google Scholar
  2. 2.
    Xiao Z, Wen H, Markham A, Trigoni N, Blunsom P, Frolik J. Non-line-of-sight identification and mitigation using received signal strength. IEEE Trans Wirel Commun. 2015;14:1689–702.CrossRefGoogle Scholar
  3. 3.
    Schmitt S, Adler S, Kyas M. The effects of human body shadowing in RF-based indoor localization. In: 30th international conference on indoor positioning and indoor navigation, 2014.Google Scholar
  4. 4.
    Saeedi S. Context-aware personal navigation services using multi-level sensor fusion algorithm. Ph.D., University of Calgary, 2013.Google Scholar
  5. 5.
    Morrison A, Renaudin V, Bancroft JB, Lachapelle G. Design and testing of a multi-sensor pedestrian location and navigation platform. Sensors. 2012;12:3720–38.CrossRefGoogle Scholar
  6. 6.
    Talvite J, Renfors M, Lohan ES. Distance-based interpolation and extrapolation methods for RSS-based localization with indoor wireless signals. IEEE Trans Veh Technol. 2015;64(4):1340–53.CrossRefGoogle Scholar
  7. 7.
    Cheng Y, Wang X, Morelande M, Moran B. Information geometry of target tracking sensor networks. Inf Fusion. 2013;14:311–26.CrossRefGoogle Scholar
  8. 8.
    Torres-solis J, Falk TH, Chau T. A review of indoor localization technologies: toward navigational assistance for topographical disorientation. Ambient Intell. 2010;51–84.Google Scholar
  9. 9.
    Bose A, Foh CH. A practical path loss model for indoor WiFi positioning enhancement. In: 6th international conference on information, communication & signal processing. IEEE; 2007. p. 1–5.Google Scholar
  10. 10.
    Niu X, Li Y, Zhang H, Wang Q, Ban Y. Fast thermal calibration of low-grade inertial sensors and inertial measurement units. Sensors. 2013;13:12192–217.CrossRefGoogle Scholar
  11. 11.
    Wang Q, Li Y, Niu X. Thermal calibration procedure and thermal characterisation of low-cost inertial measurement units. J Navig. 2015;1–18.Google Scholar
  12. 12.
    Akeila E, Salcic Z, Swain A. Reducing low-cost INS error accumulation in distance estimation using self-resetting. Trans Instrum Meas IEEE. 2014;63:177–84.CrossRefGoogle Scholar
  13. 13.
    Xie H, Gu T, Tao X, Ye H, Lv J. Maloc. A practical magnetic fingerprinting approach to indoor localization using smartphones. In: Proceedings of the 2014 ACM international joint conference on pervasive and ubiquitous computing. ACM; 2014. p. 243–53.Google Scholar
  14. 14.
    Zhang C, Subbu K, Luo J, Wu J. GROPING: geomagnetism and crowdsensing powered indoor navigation. IEEE Trans Mob Comput. 2015;14(2):387–400.CrossRefGoogle Scholar
  15. 15.
    Pritt N. Indoor navigation with use of geomagnetic anomalies. In: Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International; IEEE. 2014. p. 1859–62.Google Scholar
  16. 16.
    Li Y, Zhuang Y, Lan H, Zhang P, Niu X, El-sheimy N. WiFi-aided magnetic matching for indoor navigation with consumer portable devices. Micromachines. 2015;6:747–64.CrossRefGoogle Scholar
  17. 17.
    Chen D, Liu X, Zhang H, Li H, Weng R, Li L, Rong W, Zhang Z. A rotational gyroscope with a water-film bearing based on magnetic self-restoring effect. Sensors. 2018;18(2).CrossRefGoogle Scholar
  18. 18.
    Chen D, Liu X, Zhang H, Li H, Weng R, Li L, Rong W, Zhang Z. Friction reduction for a rotational gyroscope with mechanical support by fabrication of a biomimetic superhydrophobic surface on a ball-disk shaped rotor and the fabrication of a water film bearing. Micromachines. 2017;8(7):223.CrossRefGoogle Scholar
  19. 19.
    Zhen D, Zhao H, Gu F, Ball A. Phase-compensation-based dynamic time warping for fault diagnosis using the motor current signal. Meas Sci Technol. 2012;23:055601.CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Communication Research CenterHarbin Institute of TechnologyHarbinChina

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