An enhanced MEMS error modeling approach based on Nu-Support vector regression

Sensors (Switzerland)

Volumn 12, Issue 7, 2012, Pages 9448-9466

  Bhatt, Deepak ^a   Aggarwal, Priyanka ^a   Bhattacharya, Prabir ^b   Devabhaktuni, Vijay ^a  

^a UNIVERSITY OF TOLEDO   (United States)

^b University of Cincinnati   (United States)

Author keywords

MEMS IMU; Neural Network; Support Vector Machines

Indexed keywords

EID: 84864350958     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s120709448     Document Type: Article

References (37)

1. Venkatesh, P.; Sullivan, F. MEMS in Automotive and Consumer Electronics. In Sensors Weekly. Available online: http://www.sensorsmag.com/sensors/Automotive/MEMS-in-Automotive-and-ConsumerElectronics/ArticleStandard/473909 (accessed on 30 April 2007).
2. Maluf, N.; Williams, K. An introduction to microelectromechanical systems engineering. Meas. Sci. Technol. 2002, 13, doi:10.1088/0957-0233/13/2/701.
3. El-Sheimy, N. Inertial Techniques and INS/DGPS Integration. In Engo 623-Course Notes; Department of Geomatics Engineering, University of Calgary: Calgary, AB, Canada, 2007.
4. Aggarwal, P.; Syed, Z.; Noureldin, A.; El-Sheimy, N. Integrated MEMS Based Navigation Systems; Artech House: Norwood, MA, USA, 2011.
5. Titterton, D.; Weston, J.L. Strapdown Inertial Navigation Technology, 2nd ed.; American Institute of Aeronautics and Astronomy Press: Reston, VA, USA, 2004.
6. El-Diasty, M.; Pagiatakis, S. Calibration and stochastic modeling of inertial navigation sensor errors. JGPS 2008, 7, 170-182.
7. Walid, A.H. Accuracy Enhancement of Integrated MEMS-IMU/GPS Systems for Land Vehicular Navigation Applications. Ph.D. Thesis, Department of Geomatics Engineering, University of Calgary, Calgary, AB, Canada, 2005.
8. Skaloud, J. Optimizing Georeferencing of Airborne Survey Systems by INS/DGPS. PhD's Thesis, Department of Geomatics Engineering, University of Calgary, Calgary, AB, Canada, 1999.
9. Crista Sensors. Available online: http://www.cloudcaptech.com/crista.shtm (accessed on 22 February 2011).
10. Becker, J.; Fisher, J. Crista IMU Communications Specification. Available online: http://www.cloudcaptech.com/download/Sensors/Crista IMUSpec.pdf (accessed on 12 April 2011).
11. The MEMSIC Corporation. Available online: http://www.memsic.com/products/inertial-systems/inertial-systems.html (accessed on 20 May 2011).
12. NAV 440 GPS-aided MEMS Inertial Systems. Available online: http://www.memsic.com/support/documentation/inertial-systems/category/2-datasheets.html/nav440 (accessed on 2 January 2011).
13. Park, M.; Gao, Y. Error and performance analysis of MEMS-based inertial sensors with a low-cost GPS receiver. Sensors 2008, 8, 2240-2261.
14. Noureldin, A.; Karamat, B.; Eberts Mark, D.; El-Shafie, A. Performance enhancement of MEMS-Based INS/GPS integration for low-cost navigation applications. IEEE Trans. Veh. Technol. 2009, 58, 1077-1096.
15. Sharaf, R.; Osman, A.; El-Sheimy, N. INS/GPS Data Fusion Technique Utilizing Radial Basis Functions Neural Networks. In Proceedings of the Position Location and Navigation Symposium, Monterey, CA, USA, 26-29 April 2004; pp. 280-284.
16. Hao, W.; Tian, W. Modeling the random drift of micromachined gyroscope with Neural Network. Neural Proc. Lett. 2005, 22, 235-247.
17. Zhang, Y.S.; Yang, T. Modeling and compensation of MEMS gyroscope output data based on support vector machine. Measurement 2012, 45, 922-926.
18. Xu, G.; Meng, X. The MEMS IMU Error Modeling Analysis Using Support Vector Machines. In Proceedings of the 2009 Second International Symposium on Knowledge Acquisition and Modeling, Wuhan, China, 30 November-1 December 2009; pp. 335-337.
19. Scholkopf, B.; Smola, A.; Williamson, R.C.; Bartlett, P.L. New Support vector Algorithms. Neural Comput. 2000, 12, 1207-1245.
20. Smola, A.J.; Scholkopf, B. A tutorial on support vector regression. Stat. Comput. 2004, 14, 199-222.
21. IEEE Aerospace and Electronic Systems Society. Gyro and Accelerometer Panel; IEEE Standard Specification Format Guide and Test Procedure for Single-Axis Interferometric Optic Gyros; Institute of Electrical and Electronics Engineers: New York, NY, USA, 1998.
22. Brown, R.G.; Hwang, P. Introduction to Random Signals and Applied Kalman Filtering; Wiley Press: New York, NY, USA, 1997.
23. Maybeck, P.S. Stochastic Models, Estimation, and Control; Academic Press: New York, NY, USA, 1982; Volume 2.
24. Gelb, A. Applied Optimal Estimation; The Massachusetts Institute of Technology Press: Cambridge, MA, USA, 1974.
25. Grewal, M.S.; Andrews, A.P. Kalman Filtering Theory and Practice Using MATLAB, 3rd ed.; Wiley-IEEE press: Piscataway, NJ, USA, 2008.
26. Allan, D. Statistics of atomic frequency standards. Proc. IEEE 1996, 54, 221-230.
27. El-Sheimy, N.; Hou, H.; Niu, X. Analysis and modeling of inertial sensors using allan variance. IEEE Trans. Instrum. Meas. 2008, 57, 140-149.
28. De Pasquale, G.; Somà, A. Reliability testing procedure for MEMS IMUs applied to vibrating environments. Sensors 2010, 10, 456-474.
29. Haykin, S. Neural Networks-A Comphrensive Foundation; IEEE Press: Piscataway, NJ, USA, 1994.
30. Kurban, T.; Beşdok, E. A comparison of rbf neural network training algorithms for inertial sensor based terrain classification. Sensors 2009, 9, 6312-6329.
31. Vapnik, V.N. The nature of Statistical Learning Theory; Springer-Verlag: New York, NY, USA, 1999.
32. Cortes, C.; Vapnik, V. Support vector networks. Mach. Learn. 1995, 20, 273-297.
33. Keerthi, S.S.; Lin, C.J. Asymptotic behaviors of support vector machines with Gaussian kernel. Neural Comput. 2003, 15, 1667-1689.
34. Karush, W. Minima of Functions of Several Variables with Inequalities as Side Constraints, Master's thesis, Department of Mathematics, University of Chicago, Chicago, IL, USA, 1939.
35. Kuhn, H.W.; Tucker, A.W. Nonlinear Programming. In Proceedings of the 2nd Berkeley Symposium on Mathematical Statistics and Probabilistics, Berkeley, CA, USA, 31 July-12 August 1951; pp. 481-492.
36. Hu, L.; Che, X.; Cheng, X. Bandwidth prediction of Nu-Support vector regression and parallel hybrid particle swarm optimization. Int. J. Comput. Intell. Syst. 2009, 3, 70-83.
37. Chang, C.C.; Lin, C.J. LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol. 2011, 2, 27:1-27:27.