Comparison of Adaptive Kalman Filters in Aircraft State Estimation

Abstract

Aircraft state estimation refers to the process of determining the current or future state of an aircraft, such as its position, velocity, orientation, and other relevant parameters, based on available sensor data and mathematical models. This information is crucial for safe and efficient flight operations, as well as for various applications, including Guidance, Navigation, Control (GNC), and autonomous flight. Given the beginning circumstances, the motion of the airplane was examined in this study by estimating the state vectors using the Kalman Filter (KF) and the Adaptive Kalman Filters (AKF), as well as by comparing the various estimate techniques.