Skip to main content
SpringerLink
Log in

MEMS Gyroscopes’ Noise Simulation Algorithm

  • Conference paper
  • First Online:
Advances in Intelligent Systems and Computing IV (CSIT 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1080))

Included in the following conference series:

  • 719 Accesses

Abstract

MEMS gyroscopes are advantageous devices that promote a wide range of applications, however, they suffer from various stochastic errors some of which accumulate over time (angle random walk, bias random walk). To be able to use such devices, one should apply mathematical models of stochastic processes and hardware-software tools for investigation into noise, figure out the noise characteristics and develop an appropriate method of adaptive signal filtering. The Allan deviation plot is considered the most common tool for studying noise spectral characteristics. However, when distorted by unexpected noise components, the Allan deviation plot becomes difficult to interpret. The aim of this work is to present an algorithm for generating noise typical of real MEMS gyroscopes and its implementation as a part of a complex hardware-software tool for investigation into inertial measurement units, being developed by the authors. With such a tool for simulating noise with specific spectral characteristics, the researcher will be able to understand and explain the behavior of a MEMS gyroscope and thus fit a reasonable filtering method for it.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Höflinger, F., Müller, J., Zhang, R., Reindl, L., Burgard, W.: A wireless micro inertial measurement unit (IMU). IEEE Trans. Instrum. Meas. 62(9), 2583–2595 (2013). https://doi.org/10.1109/TIM.2013.2255977

    Article  Google Scholar 

  2. Blasch, E., Kostek, P., Pačes, P., Kramer, K.: Summary of avionics technologies. IEEE Aerosp. Electron. Syst. Mag. 30(9), 6–11 (2015). https://doi.org/10.1109/MAES.2015.150012

    Article  Google Scholar 

  3. Ahmed, H., Tahir, M.: Accurate attitude estimation of a moving land vehicle using low-cost MEMS IMU sensors. IEEE Trans. Intell. Transp. Syst. 18(7), 1723–1739 (2017). https://doi.org/10.1109/TITS.2016.2627536

    Article  Google Scholar 

  4. Buke, A., Gaoli, F., Yongcai, W., Lei, S., Zhiqi, Y.: Healthcare algorithms by wearable inertial sensors: a survey. China Commun. 12(4), 1–12 (2015). https://doi.org/10.1109/CC.2015.7114054

    Article  Google Scholar 

  5. Nemec, D., Janota, A., Hruboš, M., Šimák, V.: Intelligent real-time MEMS sensor fusion and calibration. IEEE Sens. J. 16(19), 7150–7160 (2016). https://doi.org/10.1109/JSEN.2016.2597292

    Article  Google Scholar 

  6. Lima, P.: A Bayesian approach to sensor fusion in autonomous sensor and robot networks. IEEE Instrum. Meas. Mag. 10(3), 22–27 (2007). https://doi.org/10.1109/MIM.2007.4284253

    Article  Google Scholar 

  7. Holyaka, R., Marusenkova, T.: Split Hall Structures: Parametric Analysis and Data Processing. Lambert Academic Publishing, Norderstedt (2018)

    Google Scholar 

  8. Shin, B., Kim, C., Kim, J., Lee, S., Kee, C., Kim, H., Lee, T.: Motion recognition-based 3D pedestrian navigation system using smartphone. IEEE Sens. J. 16(18), 6977–6989 (2016). https://doi.org/10.1109/JSEN.2016.2585655

    Article  Google Scholar 

  9. Zekavat, S., Buehrer, R.M.: Handbook of Position Location – Theory, Practice, and Advances. Wiley, New Jersey (2019). https://doi.org/10.1002/9781119434610.ch2

    Book  Google Scholar 

  10. Daroogheha, S., Lasky, T., Ravani, B.: Position measurement under uncertainty using magnetic field sensing. IEEE Trans. Magn. 54(12), 1–8 (2018). https://doi.org/10.1109/TMAG.2018.2873158

    Article  Google Scholar 

  11. Li, Y., Georgy, J., Niu, X., Li, Q., El-Sheimy, N.: Autonomous calibration of MEMS gyros in consumer portable devices. IEEE Sens. J. 15(7), 4062–4072 (2015). https://doi.org/10.1109/JSEN.2015.2410756

    Article  Google Scholar 

  12. Latt, W.T., Tan, U.-X., Riviere, C.N., Ang, W.T.: Transfer function compensation in gyroscope-free inertial measurement units for accurate angular motion sensing. IEEE Sens. J. 12(5), 1207–1208 (2012). https://doi.org/10.1109/JSEN.2011.2165057

    Article  Google Scholar 

  13. Huang, J., Soong, B.: Cost-aware stochastic compressive data gathering for wireless sensor networks. IEEE Trans. Veh. Technol. 68(2), 1525–1533 (2019). https://doi.org/10.1109/TVT.2018.2887091

    Article  Google Scholar 

  14. Shmaliy, Y., Zhao, S., Ahn, C.: Optimal and unbiased filtering with colored process noise using state differencing. IEEE Signal Process. Lett. 26(4), 548–551 (2019). https://doi.org/10.1109/LSP.2019.2898770

    Article  Google Scholar 

  15. Lin, X., Jiao, Y., Zhao, D.: An improved Gaussian filter for dynamic positioning ships with colored noises and random measurements loss. IEEE Access 6, 6620–6629 (2018). https://doi.org/10.1109/ACCESS.2018.2789336

    Article  Google Scholar 

  16. Allan, D., Levine, J.: A historical perspective on the development of the Allan variances and their strengths and weaknesses. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 63(4), 513–519 (2016). https://doi.org/10.1109/TUFFC.2016.2524687

    Article  Google Scholar 

  17. Guerrier, S., Molinari, R., Stebler, Y.: Theoretical limitations of Allan variance-based regression for time series model estimation. IEEE Signal Process. Lett. 23(5), 597–601 (2016). https://doi.org/10.1109/LSP.2016.2541867

    Article  Google Scholar 

  18. Won, S., Melek, W., Golnaraghi, F.: A Kalman/particle filter-based position and orientation estimation method using a position sensor/inertial measurement unit hybrid system. IEEE Trans. Industr. Electron. 57(5), 1787–1798 (2010). https://doi.org/10.1109/TIE.2009.2032431

    Article  Google Scholar 

  19. Hsu, Y., Wang, J.: Random drift modeling and compensation for MEMS-based gyroscopes and its application in handwriting trajectory reconstruction. IEEE Access 7, 17551–17560 (2019). https://doi.org/10.1109/ACCESS.2019.2895919

    Article  Google Scholar 

  20. Wang, Y.: Stochastic and dynamic modeling of MEMS gyroscopes. In: 2012 IEEE International Conference on Mechatronics and Automation, Chengdu, China, 5–8 August 2012. https://doi.org/10.1109/icma.2012.6285749

  21. Kim, D., M’Closkey, R.: Noise analysis of closed-Loop vibratory rate gyros. In: 2012 American Control Conference (ACC), Montreal, QC, Canada, 27–29 June 2012. https://doi.org/10.1109/acc.2012.6314985

  22. Kim, D., M’Closkey, R.: Spectral analysis of vibratory gyro noise. IEEE Sens. J. 13, 4361–4374 (2013). https://doi.org/10.1109/JSEN.2013.2269797

    Article  Google Scholar 

  23. Cao, H., Lv, H., Sun, Q.: Model design based on MEMS gyroscope random error. In: 2015 IEEE International Conference on Information and Automation, Lijiang, China, 8–10 August 2015. https://doi.org/10.1109/icinfa.2015.7279648

  24. Nazdrowicz, J., Napieralski, A.: Modelling, simulations and performance analysis of MEMS vibrating gyroscope in coventor MEMS+ environment. In: 2019 20th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE), Hannover, Germany, 24–27 March 2019. https://doi.org/10.1109/eurosime.2019.8724520

  25. Liu, Q., Han, B., Xu, J., Wu, M.: Random drift modeling for MEMS gyroscope based on lifting wavelet and wavelet neural network. In: 2011 International Conference on Electric Information and Control Engineering, Wuhan, China, 15–17 April 2011. https://doi.org/10.1109/iceice.2011.5778009

  26. Song, J., Shi, Z., Du, B., Wang, H.: The filtering technology of virtual gyroscope based on Taylor model in low dynamic state. IEEE Sens. J. 19, 5204–5212 (2019). https://doi.org/10.1109/JSEN.2019.2902950

    Article  Google Scholar 

  27. Georgy, J., Noureldin, A., Korenberg, M., Bayoumi, M.: Modeling the stochastic drift of a MEMS-based gyroscope in Gyro/Odometer/GPS integrated navigation. IEEE Trans. Intell. Transp. Syst. 11, 856–872 (2010). https://doi.org/10.1109/TITS.2010.2052805

    Article  Google Scholar 

  28. Woodman, O.: An Introduction to Inertial Navigation. University of Cambridge Computer Laboratory, Cambridge (2007)

    Google Scholar 

  29. Barrett, J.M.: Analyzing and modeling low-cost MEMS IMUs for use in an inertial navigation system. Master of Science degree thesis, Worchester Polytechnic Institute (2014)

    Google Scholar 

  30. M5Stack Documentation. https://buildmedia.readthedocs.org/media/pdf/m5stack/latest/m5stack.pdf

  31. Fedasyuk, D., Marusenkova, T.: Analyser and mathematical model for synthesizing noise of MEMS gyroscopes In: Proceedings of International Scientific Conference “Computer Sciences and Information Technologies” (CSIT-2019), vol. 1, pp. 109–112. IEEE (2019)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Lviv Polytechnic National University, Lviv, Ukraine

    Dmytro Fedasyuk & Tetyana Marusenkova

Authors
  1. Dmytro Fedasyuk
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tetyana Marusenkova
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tetyana Marusenkova .

Editor information

Editors and Affiliations

  1. Department of Artificial Intelligence, Lviv Polytechnic National University, Lviv, Ukraine

    Natalya Shakhovska

  2. Institute of Computer Science and Information Technologies, Lviv Polytechnic National University, Lviv, Ukraine

    Mykola O. Medykovskyy

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Fedasyuk, D., Marusenkova, T. (2020). MEMS Gyroscopes’ Noise Simulation Algorithm. In: Shakhovska, N., Medykovskyy, M.O. (eds) Advances in Intelligent Systems and Computing IV. CSIT 2019. Advances in Intelligent Systems and Computing, vol 1080. Springer, Cham. https://doi.org/10.1007/978-3-030-33695-0_62

Download citation

Publish with us

Policies and ethics

Search

Navigation