Combined System Identification and State Estimation for a Quadrotor UAV

This paper proposes a probabilistic approach for online system identification and self-calibration in small rotorcraft Unmanned Aerial Vehicles (UAVs) for improved control design and navigation. The approach integrates the system identification and state estimation processes into a single framework, allowing for self-awareness and self-healing, and uses a combination of inertial cues, dynamic modeling, and an additional sensor for convergence to the optimal value. The results are supported by simulations using realistic data in Gazebo.