T.A. Marusenkova
Èlektron. model. 2019, 41(5):03-16
https://doi.org/10.15407/emodel.41.05.003
ABSTRACT
The work presents a solution to a problem of developing software for modeling noise of MEMSThe work presents a solution to a problem of developing software for modeling noise of MEMSgyroscopes. Such software is of great importance due to complexity of the algorithms forminimization of pose estimation errors by compensation for the transfer function drift based ondigital filtering. We have proposed two algorithms for synthesizing noise terms typical of MEMSgyroscopes. The first of these algorithms is based on integrating pseudorandom harmonic signals.The second one assumes frequency correction of an array of pseudorandom signals. The spectralcharacteristics of synthesized noise are analyzed using the Allan variance. We use our ownsoftware, IMU tester, based on M5Stack with SoC ESP32, to study noise parameters. The obtainedresults are of key importance for simulation of MEMS gyroscopes errors using the Monte-Carlomethod, optimization of the correctingKalman-based filters and firmware of integrated IMUsensors.
KEYWORDS
MEMS gyroscope, noise, model of noise synthesis, inertial measurement unit.
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https://doi.org/10.1109/MAES.2015.150012
3. Ahmed, H. and Tahir, M. (2017), “Accurate attitude estimation of a moving land vehicle using low-cost MEMS IMU sensors”, IEEE Transactions on Intelligent Transportation Systems, Vol. 18, Iss. 7, pp. 1723-1739.
https://doi.org/10.1109/TITS.2016.2627536
4. Buke, A., Gaoli, F., Yongcai, W., Lei, S. and Zhiqi, Y. (2015), “Healthcare algorithms by wearable inertial sensors: a survey”, China Communications, Vol. 12, Iss. 4, pp. 1-12.
https://doi.org/10.1109/CC.2015.7114054
5. Nemec, D., Janota, A., Hrubos, M. and Simak, V. (2016), “Intelligent real-time MEMS sensor fusion and calibration”, IEEE Sensors Journal, Vol. 16, Iss. 19, pp. 7150-7160.
https://doi.org/10.1109/JSEN.2016.2597292
6. Lima, P.U. (2007), “A Bayesian approach to sensor fusion in autonomous sensor and robot networks”, IEEE Instrumentation & Measurement Magazine, Vol. 10, Iss. 3, pp. 22-27.
https://doi.org/10.1109/MIM.2007.4284253
7. Holyaka, R. and Marusenkova, T. (2018), Split Hall Structures: Parametric Analysis and Data Processing, Lambert Academic Publishing, Norderstedt, Germany.
8. (2017), The five motion senses: using MEMS inertial sensing to transform application, Analog Devices.
9. Shin, B., Kim, C., Kim, J., Lee, S., Kee, C., Kim, H. and Lee, T. (2016), “Motion recognitionbased 3D pedestrian navigation system using smartphone”, IEEE Sensors Journal, Vol. 16, Iss. 8, pp. 6977-6989.
https://doi.org/10.1109/JSEN.2016.2585655
10. Zekavat, R. and Buehrer, M. (2019), Localization sensor error measures and analysis. Handbook of Position Location: Theory, Practice, and Advances, John Wiley & Sons.
11. Daroogheha, S., Lasky, T. and Ravani, B. (2018), “Position measurement under uncertainty using magnetic field sensing”, IEEE Transactions on Magnetics, Vol. 54, Iss.12, no. 4002908.
https://doi.org/10.1109/TMAG.2018.2873158
12. Li, Y., Georgy, J., Niu, X, Li, Q. and El-Sheimy, N. (2015), “Autonomous calibration of MEMS gyros in consumer portable devices”, IEEE Sensors Journal, Vol. 15, Iss. 7, pp. 4062-4072.
https://doi.org/10.1109/JSEN.2015.2410756
13. Latt, W., Tan, U., Riviere, C. and Ang, W. (2012), “Transfer function compensation in gyroscope-free inertial measurement units for accurate angular motion sensing”, IEEE Sensors Journal, Vol. 12, Iss. 5, pp. 1207-1208.
https://doi.org/10.1109/JSEN.2011.2165057
14. Huang, J. and Soong, B. (2019), “Cost-aware stochastic compressive data gathering for wireless sensor networks”, IEEE Transactions on Vehicular Technology, Vol. 68, pp. 1525-1533.
https://doi.org/10.1109/TVT.2018.2887091
15. Shmaliy, Y., Zhao, S. and Ahn, C. (2019), “Optimal and unbiased filtering with colored process noise using state differencing”, IEEE Signal Processing Letters, Vol. 26, Iss. 4, pp. 548-551.
https://doi.org/10.1109/LSP.2019.2898770
16. Lin, X., Jiao, Y. and Zhao, D. (2018), “An improved Gaussian filter for dynamic positioning ships with colored noises and random measurements loss”, IEEE Access, Vol. 6, pp. 6620-6629.
https://doi.org/10.1109/ACCESS.2018.2789336
17. Allan, D. and Levine, J. (2016), “A historical perspective on the development of the Allan variances and their strengths and weaknesses”, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, Vol. 63, Iss. 4, pp. 513-519.
https://doi.org/10.1109/TUFFC.2016.2524687
18. Guerrier, S., Molinari, R. and Stebler, Y. (2016), “Theoretical limitations of Allan variance-based regression for time series model estimation”, IEEE Signal Processing Letters, Vol. 23, Iss. 5, pp. 597-601.
https://doi.org/10.1109/LSP.2016.2541867
19. Shao, T., Duan, Z., Ge, Q. and Liu, H. (2019), “Recursive performance ranking of Kalman filter withmismatched noise covariances”, IET Control Theory&Applications, Vol. 13, Iss. 4, pp. 459-466.
https://doi.org/10.1049/iet-cta.2018.5064
20. Won, S., Melek, W. and Golnaraghi, F. (2010), “A Kalman particle filter based position and orientation estimation method using a position sensor/Inertial measurement unit hybrid system”, IEEE Transactions on Industrial Electronics, Vol. 57, Iss. 5, pp. 1787-1798.
https://doi.org/10.1109/TIE.2009.2032431
21. (2017), High Stability, Low Noise Vibration Rejecting Yaw Rate Gyroscope. Data Sheet ADXRS646, Analog Device, available at https://www.analog.com/media/en/technical-documentation/data-sheets/ADXRS646.pdf (accessed June 10 2019).
22. (2018), “M5Stack Documentation”, available at https://media.readthedocs.org/pdf/m5stack/master/m5stack.pdf (accessed April 13 2019).
23. (2014), “MICRO-CAP. Electronic Circuit Analysis Program. Spectrum Software”, available at http://www.spectrum-soft.com (accessed April 13 2019).