Hostname: page-component-848d4c4894-mwx4w Total loading time: 0 Render date: 2024-06-15T07:34:34.288Z Has data issue: false hasContentIssue false
  • English
  • Français

A new method to reduce the noise of the miniaturised inertial sensors disposed in redundant linear configurations

Published online by Cambridge University Press:  27 January 2016

T. L. Grigorie  and
R. M. Botez
T. L. Grigorie*
Affiliation:
University of Craiova, Faculty of Electrical Engineering, Dolj, Romania
R. M. Botez*
Affiliation:
École de Technologie Supérieure, Montreal, Canada
*
ltgrigorie@yahoo.com
Ruxandra.Botez@etsmtl.ca
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper presents a new adaptive algorithm for the statistical filtering of miniaturised inertial sensor noise. The algorithm uses the minimum variance method to perform a best estimate calculation of the accelerations or angular speeds on each of the three axes of an Inertial Measurement Unit (IMU) by using the information from some accelerometers and gyros arrays placed along the IMU axes. Also, the proposed algorithm allows the reduction of both components of the sensors’ noise (long term and short term) by using redundant linear configurations for the sensors dispositions.

A numerical simulation is performed to illustrate how the algorithm works, using an accelerometer sensor model and a four-sensor array (unbiased and with different noise densities). Three cases of ideal input acceleration are considered: 1) a null signal; 2) a step signal with a no-null time step; and 3) a low frequency sinusoidal signal.

To experimentally validate the proposed algorithm, some bench tests are performed. In this way, two sensors configurations are used: 1) one accelerometers array with four miniaturised sensors (n = 4); and 2) one accelerometers array with nine miniaturised sensors (n = 9). Each of the two configurations are tested for three cases of input accelerations: 0ms−1, 9·80655m/s2 and 9·80655m/s2.

Type
Research Article
Information
The Aeronautical Journal , Volume 117 , Issue 1188 , February 2013 , pp. 111 - 132
Copyright
Copyright © Royal Aeronautical Society 2013 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Barbour, N.M. Inertial Navigation Sensors, NATO RTO-EN-SET-116, 2010.Google Scholar
2. Barbour, N.M. and Schmidt, G. Inertial sensor technology trends, IEEE Sensors J, 2001, 1, (4), pp 332–339.Google Scholar
3. Kraft, M. Micromachined Inertial Sensors State of the Art and a Look into the Future, University of Southampton, Highfield, UK.Google Scholar
4. Schmidt, G. INS/GPS Technology Trends. NATO RTO-EN-SET-116, 2010.Google Scholar
5. Weinberg, M. and Kourepenis, A. Error sources in in-plane silicon tuning fork MEMS gyroscopes, JMEMS, 2006, 15, (3), pp 479 – 491.Google Scholar
6. Titterton, D.H. Strapdown inertial navigation technology (2nd ed), Institution of Engineering and Technology, 2004.Google Scholar
7. Hasan, A.M., Samsudin, K., Ramli, A.R. and Azmir, R.S. Wavelet-based pre-filtering for low cost inertial sensors, J Applied Science, 2010, 10, pp 2217–2230.Google Scholar
8. Mao, B., Wu, J.W., Wu, J.T. and Zhou, X.M. MEMS Gyro denoising based on second generation wavelet transform first Int conf on pervasive computing, Signal Proc and App, China, pp 255–258, 2010.Google Scholar
9. Sabat, S.L., Giribabu, N., Nayak, J. and Krishnaprasad, K. Characterization of Fiber Optics Gyro and Noise Compensation Using Discrete Wavelet Transform. Second International Conference on Emerging Trends in Engineering & Technology, 2009, India, pp 909–913.Google Scholar
10. Skaloud, B., Bruton, A.M. and Schwartz, K. Detection and filtering of short-term (1/f) noise in inertial sensors, J Institute of Navigation, 1999, 46, (2), pp 97–107.Google Scholar
11. Grigorie, T.L., Edu, I.R. and Corcau, J.I. Fuzzy logic denoising of the miniaturized inertial sensors in redundant configurations, 33rd International Conference on Information Technology Interfaces (ITI 2011), Dubrovnik, Croatia, 27-30 June, 2011.Google Scholar
12. Grewal, M.S., Weill, L.R. and Andrews, A.P. Global Positioning Systems, Inertial Navigation, and Integration, John Wiley and Sons, 2001.Google Scholar
13. Siouris, G.M. Aerospace Avionics Systems: A Modern Synthesis, Academic Press, 1993.Google Scholar
14. Radix, J.C. Systèmes inertiels à composants liés ≪Strap-Down≫. Cepadues-Editions, SUP’AERO, Toulouse, France, 1993.Google Scholar
15. Guerrier, S. Integration of Skew-Redundant MEMS-IMU with GPS for Improved Navigation Performance, Master Project, Ecole Polytechnique Federale de Lausanne, Franc, June 2008.Google Scholar
16. Pejsa, A.J. Optimum skewed redundant inertial navigators, AIAA J, 1974, 12, pp 899–902.Google Scholar
17. Allerton, D. and Jia, H. An error compensation method for skewed redundant inertial configuration. Proceedings of the ION 58th Annual Meeting and CIGTF 21st Guidance Test Symposium, Albuquerque, NM, USA, pp 142–147, 2002.Google Scholar
18. Guerrier, S. Improving Accuracy with Multiple Sensors: Study of Redundant MEMS-IMU/GPS Configurations, Proceedings of the 22nd International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS 2009), Savannah, GA, USA, September 2009, pp 3114–3121.Google Scholar
19. Seong, Y.C. and Chan, G.P. Calibration of a redundant IMU, AIAA Guidance, Navigation and Control Conference and Exhibit, 16-19 August, Rhode Island, USA, 2004.Google Scholar
20. Seong, Y.C. and Chan, G.P. A calibration technique for a redundant IMU containing Low-Grade Inertial Sensors, ETRI J, August 2005, 27, (4), pp 418–426.Google Scholar
21. Grigorie, T.L. and Botez, R.M. Miniaturised inertial sensors’ noise reduction by using redundant linear configurations. The Nineteenth IASTED International Conference on Applied Simulation and Modelling, Crete, Greece, 22-24 June, 2011.Google Scholar
22. Varshney, P.K. Multisensor data fusion, Electronics and Communications Eng J, December 1997, 9, pp 245–253.Google Scholar
23. Punska, O. Bayesian Approaches to Multi-Sensor Data Fusion, Master of Philosophy degree dissertation, St John’s College, Department of Engineering, University of Cambridge, Cambridge, UK, August 31, 1999.Google Scholar
24. Grigorie, T.L., Lungu, M., Edu, I.R. and Obreja, R. Concepts for error modeling of miniature accelerometers used in inertial navigation systems, Annals of the University of Craiova, Electrical Engineering series, 2010, 34, pp 212–219.Google Scholar