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. - References

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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.

has been cited by the following article:

TITLE: Particle Filter Data Fusion Enhancements for MEMS-IMU/GPS

AUTHORS: Yafei Ren, Xizhen Ke

KEYWORDS: Micro-Electro-Mechanical-System, Particle Filter, Data Fusion, Extended Kalman Filtering

JOURNAL NAME: Intelligent Information Management, Vol.2 No.7, July 22, 2010

ABSTRACT: 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.