Comparative Analysis of Linear and Nonlinear Kalman Filters for Airflow Estimation in UAVs

In the past three decades, numerous works have been done on the applications of linear and nonlinear Kalman filters in estimating crewed and uncrewed aircraft airflow angles. In uncrewed autonomous aircraft, the flight envelope protection (FEP) algorithms play a vital role in ensuring the safety of the aircraft during flight. The FEP heavily relies on accurate estimations of angle of attack and sideslip angles. In addition to safety, autonomous controller performance can significantly degrade due to poor airflow estimations. Compared to large transport or general aviation aircraft, autonomous aircraft are much lighter, fly lower, and fly slower, which makes them more vulnerable to external disturbances. This work presents the theoretical framework of both linear and nonlinear Kalman filters. It showcases the design process of five different Kalman filters using the
$6\text{DoF}$
simulation environment in the presence of sensor noise and external disturbances in the form of the Dryden wind disturbance model. Different Kalman filter designs are assessed using actual flight test data for a realistic evaluation process. Among the five different designs, the Ensemble Kalman filter demonstrated the lowest mean normal of the covariance matrix, indicating superior estimation accuracy. Given the stringent computation power onboard uncrewed aircraft, special attention is given to the computational overhead of each design.