Skip to main content
Log in

Stochastic Error Modeling of Smartphone Inertial Sensors for Navigation in Varying Dynamic Conditions

  • Published:
Gyroscopy and Navigation Aims and scope Submit manuscript

Abstract

This paper aims at investigating and analyzing the behavior of Micro-Electromechanical Systems (MEMS) inertial sensors stochastic errors in both static and varying dynamic conditions using two MEMSbased Inertial Measurement Units (IMUs) of two different smartphones. The corresponding stochastic error processes were estimated using two different methods, the Allan Variance (AV) and the Generalized Method of Wavelets Moments (GMWM). The developed model parameters related to laboratory dynamic environment are compared to those obtained under static conditions. A contamination test was applied to all data sets to distinguish between clean and corrupted ones using a Confidence Interval (CI) investigation approach. A detailed analysis is presented to define the link between the error model parameters and the augmented dynamics of the tested smartphone platform. The paper proposes a new dynamically dependent integrated navigation algorithm which is capable of switching between different stochastic error parameters values according to the platform dynamics to eliminate dynamics-dependent effects. Finally, the performance of different stochastic models based on AV and GMWM were analyzed using simulated Inertial Navigation System (INS)/Global Positioning System (GPS) data with induced GPS signal outages through the new proposed dynamically dependent algorithm. The results showed that the obtained position accuracy is improved when using dynamic-dependent stochastic error models, through the adaptive integrated algorithm, instead of the commonly used static one, through the non-adaptive integrated one. The results also show that the stochastic error models from GMWM-based model structure offer better performance than those estimated from the AV-based model.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. A. B. Jensen, “GNSS Satellite Orbits,” Technical University of Denmark, 2010.

    Google Scholar 

  2. N. El-Sheimy, “Lecture note 623–Inertial Techniques and INS/DGPS Integration,” ed: Department of Geomatics Engineering, University of Calgary, 2014.

    Google Scholar 

  3. S. Nassar, Z. Syed, X. Niu, and N. El-Sheimy, “Improving MEMS IMU/GPS systems for accurate land-based navigation applications,” in ION NTM, 2006, pp. 523–529.

    Google Scholar 

  4. O. J. Woodman, “An introduction to inertial navigation,” University of Cambridge, Computer Laboratory, Tech. Rep. UCAMCL-TR-696, vol. 14, p. 15, 2007.

    Google Scholar 

  5. X. Niu, S. Nasser, C. Goodall, and N. El-Sheimy, “A universal approach for processing any MEMS inertial sensor configuration for land-vehicle navigation,” The Journal of Navigation, vol. 60, no. 2, pp. 233–245, 2007.

    Article  Google Scholar 

  6. S. Nassar, Improving the inertial navigation system (INS) error model for INS and INS/DGPS applications. National Library of Canada= Bibliothèque nationale du Canada, 2005.

    Google Scholar 

  7. Y. Li, J. Georgy, X. Niu, Q. Li, and N. El-Sheimy, “Autonomous calibration of MEMS gyros in consumer portable devices,” IEEE Sensors Journal, vol. 15, no. 7, pp. 4062–4072, 2015.

    Article  Google Scholar 

  8. X. Niu, Z. Gao, R. Zhang, Z. Chen, and J. Dong, “Micro-machined attitude measurement unit with application in satellite TV antenna stabilization,” in Symposium Gyro Technology 2002, Stuttgart, Germany, 2002, p. 2002.

    Google Scholar 

  9. Y. Li, H. Lan, Y. Zhuang, P. Zhang, X. Niu, and N. El-Sheimy, “Real-time attitude tracking of mobile devices,” in Indoor Positioning and Indoor Navigation (IPIN), 2015 International Conference on, 2015, pp. 1–7: IEEE.

    Google Scholar 

  10. N. El-Sheimy, H. Hou, and X. Niu, “Analysis and modeling of inertial sensors using Allan variance,” IEEE Transactions on instrumentation and measurement, vol. 57, no. 1, pp. 140–149, 2008.

    Article  Google Scholar 

  11. J. R. Evans et al., “Method for calculating self-noise spectra and operating ranges for seismographic inertial sensors and recorders,” Seismological research letters, vol. 81, no. 4, pp. 640–646, 2010.

    Article  Google Scholar 

  12. P. Petkov and T. Slavov, “Stochastic modeling of MEMS inertial sensors,” Cybernetics and information technologies, vol. 10, no. 2, pp. 31–40, 2010.

    Google Scholar 

  13. D. Magill, “Optimal adaptive estimation of sampled stochastic processes,” IEEE Transactions on Automatic Control, vol. 10, no. 4, pp. 434–439, 1965.

    Article  MathSciNet  Google Scholar 

  14. S. Dmitriev, D. Koshaev, and O. Stepanov, “Multichannel filtration and its application in removing ambi guity when positioning objects by using the GPS,” Journal of Computer and Systems Sciences International, vol. 36, no. 1, pp. 57–62, 1997.

    MATH  Google Scholar 

  15. X. R. Li and V. P. Jilkov, “Survey of maneuvering target tracking. Part V. Multiple-model methods,” IEEE Transactions on Aerospace and Electronic Systems, vol. 41, no. 4, pp. 1255–1321, 2005.

    Article  Google Scholar 

  16. D. W. Allan, “Statistics of atomic frequency standards,” Proceedings of the IEEE, vol. 54, no. 2, pp. 221–230, 1966.

    Article  Google Scholar 

  17. X. Zhang, Y. Li, P. Mumford, and C. Rizos, “Allan variance analysis on error characters of MEMS inertial sensors for an FPGA-based GPS/INS system,” in Proceedings of the International Symposium on GPS/GNNS, 2008, pp. 127–133.

    Google Scholar 

  18. M. Marinov and Z. Petrov, “Allan variance analysis on error characters of low-cost MEMS accelerometer MMA8451Q,” in International conference of scientific paper AFASES, 2014.

    Google Scholar 

  19. J. Li and J. Fang, “Not fully overlapping Allan variance and total variance for inertial sensor stochastic error analysis,” IEEE Transactions on Instrumentation and Measurement, vol. 62, no. 10, pp. 2659–2672, 2013.

    Article  Google Scholar 

  20. A. Hussen and I. Jleta, “Low Cost Inertial Sensors Modeling Using Allan Variance,” World Academy of Science, Engineering and Technology, International Journal of Computer, Electrical, Automation, Control and Information Engineering, vol. 9, no. 5, pp. 1237–1242, 2015.

    Google Scholar 

  21. S. Guerrier, J. Skaloud, Y. Stebler, and M.-P. Victoria-Feser, “Wavelet-variance-based estimation for composite stochastic processes,” Journal of the American Statistical Association, vol. 108, no. 503, pp. 1021–1030, 2013.

    Article  MathSciNet  MATH  Google Scholar 

  22. J. Balamuta, R. Molinari, S. Guerrier, and W. Yang, “The gmwm R package: a comprehensive tool for time series analysis from state-space models to robustness,” arXiv preprint arXiv:1607.04543, 2016.

    Google Scholar 

  23. J. Balamuta, S. Guerrier, R. Molinari, and W. Yang, “A Computationally Efficient Framework for Automatic Inertial Sensor Calibration,” arXiv preprint arXiv:1603.05297, 2016.

    Google Scholar 

  24. S. Guerrier, R. Molinari, and Y. Stebler, “Wavelet-Based Improvements for Inertial Sensor Error Modeling,” IEEE Transactions on Instrumentation and Measurement, vol. 65, no. 12, pp. 2693–2700, 2016.

    Article  Google Scholar 

  25. Y. Yuksel, N. El-Sheimy, and A. Noureldin, “Error modeling and characterization of environmental effects for low cost inertial MEMS units,” in Position Location and Navigation Symposium (PLANS), 2010 IEEE/ION, 2010, pp. 598–612: IEEE.

    Chapter  Google Scholar 

  26. P. Aggarwal, Z. Syed, X. Niu, and N. El-Sheimy, “Cost-effective testing and calibration of low cost MEMS sensors for integrated positioning, navigation and mapping systems,” in Proceedings of XXIII FIG Congress, Munich, Germany, 2006, vol.813.

  27. M. El-Diasty, A. El-Rabbany, and S. Pagiatakis, “Temperature variation effects on stochastic characteristics for low-cost MEMS-based inertial sensor error,” Measurement Science and Technology, vol. 18, no. 11, p. 3321, 2007.

    Article  Google Scholar 

  28. M. Wis and I. Colomina, “Dynamic dependency of inertial sensor errors and its application to INS/GNSS navigation,” Proc. NAVITEC Congr, pp. 1–7, 2010.

    Google Scholar 

  29. Y. Stebler, S. Guerrier, J. Skaloud, R. Molinari, and M.-P. Victoria-Feser, “Study of MEMS-based inertial sensors operating in dynamic conditions,” in 2014 IEEE/ION Position, Location and Navigation Symposium-PLANS 2014, 2014, pp. 1227–1231: IEEE.

    Chapter  Google Scholar 

  30. A. Radi, N. Elsheimy, and Y. Li, “Temperature Variation effects on the Stochastic Performance of Smartphone Sensors Using Allan Variance and Generalized Method of Wavelet Moments,” ed. Proceedings of the 2017 International Technical Meeting of The Institute of Navigation, Monterey, California, January 2017, pp. 1242–1255, 2017.

    Google Scholar 

  31. E. Shin and N. El-Sheimy, “Aided Inertial Navigation System (AINS™) Toolbox for MatLab® Software,” INS/GPS integration software, Mobile Multi-Sensors System (MMSS) research group, the University of Calgary http://mms.geomatics.ucalgary.ca/Research/Tech% 20transfer/INS_toolbox.htm, 2004.

    Google Scholar 

  32. A. G. Hayal, “Static calibration of the tactical grade inertial measurement units,” The Ohio State University, 2010.

    Google Scholar 

  33. P. G. Savage, “Analytical modeling of sensor quantization in strapdown inertial navigation error equations,” Journal of Guidance, Control, and Dynamics, vol. 25, no. 5, pp. 833–842, 2002.

    Article  Google Scholar 

  34. I. o. E. a. E. Engineers, IEEE Standard Specification Format Guide and Test Procedure for Linear, Single-axis, Nongyroscopic Accelerometers. IEEE, 1999.

    Google Scholar 

  35. W. Abdel-Hamid, Accuracy enhancement of integrated MEMS-IMU/GPS systems for land vehicular navigation applications. Library and Archives Canada= Bibliothèque et Archives Canada, 2006.

    Google Scholar 

  36. S. Guerrier, R. Molinari, and Y. Stebler, “Theoretical Limitations of Allan Variance-based Regression for Time Series Model Estimation,” IEEE Signal Processing Letters, vol. 23, no. 5, pp. 597–601, 2016.

    Article  Google Scholar 

  37. X. Niu et al., “Using Allan variance to analyze the error characteristics of GNSS positioning,” GPS solutions, vol. 18, no. 2, pp. 231–242, 2014.

    Article  Google Scholar 

  38. H. Hou, Modeling inertial sensors errors using Allan variance. Library and Archives Canada= Bibliothèque et Archives Canada, 2005.

    Google Scholar 

  39. R. J. Vaccaro and A. S. Zaki, “Statistical modeling of rate gyros,” IEEE Transactions on Instrumentation and Measurement, vol. 61, no. 3, pp. 673–684, 2012.

    Article  Google Scholar 

  40. D. B. Percival and P. Guttorp, “Long-memory processes, the Allan variance and wavelets,” Wavelets in geophysics, vol. 4, pp. 325–344, 1994.

  41. D. B. Percival and A. T. Walden, Wavelet methods for time series analysis. Cambridge university press, 2006.

    MATH  Google Scholar 

  42. Y. Stebler, S. Guerrier, J. Skaloud, and M.-P. Victoria-Feser, “A framework for inertial sensor calibration using complex stochastic error models,” in Position Location and Navigation Symposium (PLANS), 2012 IEEE/ION, 2012, pp. 849–861: Ieee.

    Chapter  Google Scholar 

  43. S. Guerrier and R. Molinari, “Wavelet variance for random fields: an m-estimation framework,” arXiv preprint arXiv:1607.05858, 2016.

    Google Scholar 

  44. S. Guerrier, R. Molinari, and J. Skaloud, “Automatic identification and calibration of stochastic parameters in inertial sensors,” Navigation, vol. 62, no. 4, pp. 265–272, 2015.

    Article  Google Scholar 

  45. Y. Stebler, S. Guerrier, J. Skaloud, and M.-P. Victoria-Feser, “Generalized method of wavelet moments for inertial navigation filter design,” IEEE Transactions on Aerospace and Electronic Systems, vol. 50, no. 3, pp. 2269–2283, 2014.

    Article  MATH  Google Scholar 

  46. A. IFIXIT, “Apple iPhone 5s.”

  47. T. R. Kane and D. A. Levinson, Dynamics, theory and applications. McGraw Hill, 1985.

    Google Scholar 

  48. W. Ding, J. Wang, Y. Li, P. Mumford, and C. Rizos, “Time synchronization error and calibration in integrated GPS/INS systems,” ETRI journal, vol. 30, no. 1, pp. 59–67, 2008.

    Article  Google Scholar 

  49. Y. Stebler, S. Guerrier, and J. Skaloud, “An approach for observing and modeling errors in MEMS-based inertial sensors under vehicle dynamic,” IEEE Transactions on Instrumentation and Measurement, vol. 64, no. 11, pp. 2926–2936, 2015.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ahmed Radi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Radi, A., Nassar, S. & El-Sheimy, N. Stochastic Error Modeling of Smartphone Inertial Sensors for Navigation in Varying Dynamic Conditions. Gyroscopy Navig. 9, 76–95 (2018). https://doi.org/10.1134/S2075108718010078

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S2075108718010078

Keywords

Navigation