UAV Altitude Estimation Using Kalman Filter and Extended Kalman Filter

Most navigation systems, including Unmanned aerial vehicles (UAVs), employ sensors to identify location and orientation in real time. Sensor fusion algorithms are used to merge several sensors to increase sensor data accuracy. However, the data collected may be erroneous due to noise generated by magnetic disturbances. In order to address the issue of noisy information from sensor fusion, two alternative Kalman filtering techniques will be employed in this research. The Backstepping control purpose is to follow a desired path while simultaneously controlling altitude and orientation. For improved reliability, the physical modeling technique used in this work explicitly accounts the model uncertainty.