An Implementation of Ensemble and Extended Filtering Methods to Estimate Drag and Yaw Coefficients on Amphibious Aircraft Trajectories

Indonesia is a strategically significant archipelagic nation with approximately two-thirds of its territory consisting of water. This geographical condition gives Indonesia greater potential than other countries. Amphibious aircraft serve as an alternative solution for the mobility and utility of residents living in remote areas surrounded by water, ensuring that these individuals can benefit from fair and equitable government services and their continuous development is necessary to maximize their functionality. This development encompasses various aspects, including the accuracy of flight trajectory estimation. Several machine learning methods have been developed for estimating the flight trajectory of amphibious aircraft. The present study implements and compares two filtering methods, namely the Ensemble Kalman Filter (EnKF) and the Extended Kalman Filter (EKF). The simulation result indicates that the EnKF method achieved a Root Mean Square Error (RMSE) value of 0.0214 for estimating the drag coefficient (CD) and the EKF method attained 0.0186. Furthermore, the EnKF method recorded an RMSE value of 0.0015 for estimating the yaw coefficient (CY), while the EKF method achieved 0.0012.