Particle Filter Data Fusion Enhancements for MEMS-IMU/GPS
Yafei Ren, Xizhen Ke
DOI: 10.4236/iim.2010.27051   PDF    HTML     11,341 Downloads   19,875 Views   Citations


This research aims at enhancing the accuracy of navigation systems by integrating GPS and Mi-cro-Electro-Mechanical-System (MEMS) based inertial measurement units (IMU). Because of the conditions re-quired by the large number of restrictions on empirical data, a conventional Extended Kalman Filtering (EKF) is limited to apply in navigation systems by integrating MEMS-IMU/GPS. In response to non-linear non-Gaussian dynamic models of the inertial sensors, the methods rely on a particle cloud representation of the filtering distribution which evolves through time using importance sampling and resampling ideas. Then Particle Filtering (PF) can be used to data fusion of the inertial information and real-time updates from the GPS location and speed of information accurately. The experiments show that PF as opposed to EKF is more effective in raising MEMS-IMU/GPS navigation system’s data integration accuracy.

Share and Cite:

Ren, Y. and Ke, X. (2010) Particle Filter Data Fusion Enhancements for MEMS-IMU/GPS. Intelligent Information Management, 2, 417-421. doi: 10.4236/iim.2010.27051.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] P. Lang, A. Kusej, A. Pinz and G. Brasseur, “Inertial Tracking for Mobile Augmented Reality,” IEEE Instrumentation and Measurement Technology Conference, Anchorage, AK, USA, 2002, pp. 1583-1587.
[2] E. Marchand, P. Bouthemy and F. Chaumette, “A 2D-3D Model-Based Approach to Real-Time Visual Tracking,” Image Vision Computer, Vol. 19, No. 13, 2001, pp. 941-955.
[3] M. S. Grewal, L. R. Weill and A. P. Andrews, “Global Positioning Systems, Inertial Navigation, and Integration,” 2nd Edition, John Wiley & Sons, Hoboken, 2007.
[4] Y. Yi and D. A. Grejner-Brzezinska, “Nonlinear Bayesian Filter: Alternative to the Extended Kalman Filter in the GPS/INS Fusion Systems,” Proceedings of the Institute of Navigation, ION GNSS 2005, Long Beach, CA, 2005, pp. 1391-1400.
[5] Y. Kubo, T. Sato and S. Sugimoto, “Modified Gaussian Sum Filtering Methods for INS/GPS Integration,” Journal of Global Positioning Systems, Vol. 6, No. 1, 2007, pp. 65-73.
[6] M. Nishiyama, S. Fujioka, Y. Kubo, T. Sato and S. Sugimoto, “Performance Studies of Nonlinear Filtering Methods in INS/GPS In-Motion Alignment,” Proceedings of the Institute of Navigation, ION GNSS 2006, Fort Worth, TX, 2006, pp. 2733-2742.
[7] L. Chai, W. Hoff and T. Vincent, “Three-Dimensional Motion and Structure Estimation Using Inertial Sensors and Computer Vision for Augmented Reality,” Presence: Teleoperators and Virtual Environments, Vol. 11, No. 5, 2002, pp. 474-492.
[8] M. Maidi, F. Ababsa and M. Mallem, “Vision-Inertial System Calibration for Tracking in Augmented Reality,” Proceedings of the 2nd International Conference on Informatics in Control, Automation and Robotics, Barcelona, 2005, pp. 156-162.
[9] S. You and U. Neumann, “Fusion of Vision and Gyro Tracking for Robust Augmented Reality Registration,” Proceedings of IEEE Conference on Virtual Reality, Washington, DC, 2001, pp. 71-77.
[10] A. Doucet, N. J. Gordon and V. Krishnamurthy, “Particle Filters for State Estimation of Jump Markov Linear Systems,” IEEE Transactions on Signal Processing, Vol. 49, No. 3, 2001, pp. 613-624.
[11] N. Bergman and A. Doucet, “Markov Chain Monte Carlo data Association for Target Tracking,” Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, Istanbul, 2000, pp. 705-708.
[12] N. J. Gordon, “A Hybrid Bootstrap Filter for Target Tracking in Clutter,” IEEE Transactions on Aerospace and Electronic Systems, Vol. 33, No. 1, 1997, pp. 353- 358.
[13] A. Doucet, N. de Freitas and N. Gordon, “An Introduction to Sequential Monte Carlo Methods,” In: A. Doucet, N. de Freitas and N. Gordon, Eds., Sequential Monte Carlo Methods in Practice, Springer, New York, 2001, pp. 3-14.

Copyright © 2024 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.