Table of Contents Author Guidelines Submit a Manuscript
Journal of Applied Mathematics
Volume 2014, Article ID 451939, 8 pages
http://dx.doi.org/10.1155/2014/451939
Research Article

An Adaptive Unscented Kalman Filtering Algorithm for MEMS/GPS Integrated Navigation Systems

1Marine Navigation Research Institute, College of Automation, Harbin Engineering University, Harbin 150001, China
2LASSENA Laboratoire, Ecole de Technologie Superieure, Université du Québec, Montréal, Canada H3C 1K3

Received 13 November 2013; Accepted 15 February 2014; Published 20 March 2014

Academic Editor: Yuxin Zhao

Copyright © 2014 Jianhua Cheng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. S. Mizukami, M. Sugiura, Y. Muramatsu, and H. Kumagai, “MEMS GPS/INS for micro air vehicle application,” in Proceedings of the 17th International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS '04), pp. 819–824, September 2004. View at Scopus
  2. X. Niu, S. Nassar, and N. El-Sheimy, “An accurate land-vehicle MEMS IMU/GPS navigation system using 3D auxiliary velocity updates,” Navigation, Journal of the Institute of Navigation, vol. 54, no. 3, pp. 177–188, 2007. View at Google Scholar · View at Scopus
  3. S. Y. Cho and B. D. Kim, “Adaptive IIR/FIR fusion filter and its application to the INS/GPS integrated system,” Automatica, vol. 44, no. 8, pp. 2040–2047, 2008. View at Publisher · View at Google Scholar · View at Scopus
  4. S. Y. Cho and W. S. Choi, “Robust positioning technique in low-cost DR/GPS for land navigation,” IEEE Transactions on Instrumentation and Measurement, vol. 55, no. 4, pp. 1132–1142, 2006. View at Publisher · View at Google Scholar · View at Scopus
  5. A. Gelb, Applied Optimal Estimation, The MIT press, 1974.
  6. C. Hide, T. Moore, and M. Smith, “Adaptive Kalman filtering for low-cost INS/GPS,” Journal of Navigation, vol. 56, no. 1, pp. 143–152, 2003. View at Publisher · View at Google Scholar · View at Scopus
  7. C. W. Hu, Congwei, W. Chen, Y. Chen, and D. Liu, “Adaptive Kalman filtering for vehicle navigation,” Journal of Global Positioning Systems, vol. 2, no. 1, pp. 42–47, 2003. View at Publisher · View at Google Scholar
  8. A. H. Mohamed and K. P. Schwarz, “Adaptive Kalman filtering for INS/GPS,” Journal of Geodesy, vol. 73, no. 4, pp. 193–203, 1999. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  9. D. Simon, Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches, Wiley, 2006.
  10. S. J. Julier and K. U. Jeffrey, “A general method for approximating nonlinear transformations of probability distributions,” Tech. Rep., Robotics Research Group, University of Oxford, Oxford, UK, 1996. View at Google Scholar
  11. Z. Jiang, Q. Song, Y. He, and J. Han, “A novel adaptive unscented kalman filter for nonlinear estimation,” in Proceedings of the 46th IEEE Conference on Decision and Control (CDC '07), pp. 4293–4298, New Orleans, LA, USA, December 2007. View at Publisher · View at Google Scholar · View at Scopus
  12. S. J. Julier, “The scaled unscented transformation,” in Proceedings of the American Control COnference, pp. 4555–4559, May 2002. View at Scopus
  13. S. Särkkä, “On unscented Kalman filtering for state estimation of continuous-time nonlinear systems,” IEEE Transactions on Automatic Control, vol. 52, no. 9, pp. 1631–1641, 2007. View at Publisher · View at Google Scholar · View at Scopus
  14. L. Zhao, X.-X. Wang, H.-X. Xue, and Q.-X. Xia, “Design of unscented Kalman filter with noise statistic estimator,” Control and Decision, vol. 24, no. 10, pp. 1483–1488, 2009. View at Google Scholar · View at Zentralblatt MATH · View at Scopus