Particle Filter Data Fusion Enhancements for MEMS-IMU/GPS

This research aims at enhancing the accuracy of navigation systems by integrating GPS and Micro-Electro-Mechanical-System (MEMS) based inertial measurement units (IMU). Because of the conditions required by the large number of restrictions on empirical data, a conventional Extended Kalman Filtering (EKF) is lim-ited 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 Fil-tering (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