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Unmanned Aerial Vehicle (UAV) Attitude Estimation Using Artificial Neural Network Approach

There is a growing interest in Unmanned Aerial Vehicles (UAV) which are used in various applications such as cinematography, security, entertainment, and research and development. For a UAV to be able to these applications, stability is a vital aspect. Inertial Measurement Unit (IMU) which is composed of accelerometers, and gyroscopes, and separate magnetometer give data for the attitude position of the UAV to be known and maintain a steady flight. Attitude estimation can be done by various techniques such as using an Extended Kalman Filter (EKF) to predict and estimate angular positions based on the sensor data. In this paper, an Artificial Neural Network (ANN) approach is used to estimate the angular positions as an option for the EKF. A nonlinear autoregressive with exogenous inputs (NARX) is used to create the attitude estimation to investigate the performance compared to the EKF.