Abstract
This paper describes the adaptive extended Kalman filtering which is applied to low-cost MEMS IMU/GPS integration. Unmanned Aerial Vehicles are versatile flying machine capable of handling both military and civilian missions. The availability of low-cost MEMS IMU has made it possible to construct inexpensive, integrated systems for usage in UAV applications. The adaptive extended Kalman filtering is applied to fuse the information from low-cost MEMS IMU and a Global Positioning System receiver. The maximum likelihood estimator of Myers and Tapley which could be used to online estimate the process noise is presented. Finally, the simulation result shows the effectiveness of adaptive extended Kalman filtering.
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