Implementation of Sensor Filters and Altitude Estimation of Unmanned Aerial Vehicle using Kalman Filter

Abstract

Sensor outputs are used when finding the position of an Autonomous Aerial Vehicle (UAV). Outputs such as air pressure, vehicle acceleration or magnetic field help the vehicle decide its attitude and altitude. However, it must also predict the next movement of the vehicle. The Kalman Filter is used to assist observing and predicting the state of a system. To achieve this, sensors and a model that can predict the future movement of the vehicle are needed. In practice, the sensors and models are not perfect. There are always uncertainties like weather conditions. Also the data from the sensors are often noisy. Getting less noisy data for an unmanned aerial vehicle is important for stable flight. This uncertainty is reduced with the Kalman Filter. The mentioned uncertainties can be reduced using the Kalman Filter and more stable data can be obtained.