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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.
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.
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