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.
Highlights
Inertial sensors are widely used for navigation systems [1]
Because of the conditions required 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-inertial measurement units (IMU)/GPS
The experiments show that Particle Filtering (PF) as opposed to EKF is more effective in raising MEMS-IMU/GPS navigation system’s data integration accuracy
Summary
Inertial sensors are widely used for navigation systems [1]. Compared to GPS tracking result, inertial tracking offers attractive complementary features. No external information other than initial pose estimation is required. Lang et al [1,2] show that inertial sensors can provide a good signal-to-noise ratio, especially in cases of rapid directional change (acceleration/deceleration) and for high rotational speed. Since inertial sensors only measure the variation rate or accelerations, the output signals have to be integrated to obtain the position and orientation data. Longer integrated time produces significant accumulated drift because of noise or bias
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