Adaptive Unscented Kalman Filter Based Sensor Fusion for Aircraft Positioning Relative to Unknown Runway
Nabarun Ghosh, Arnab Maity · 2025
This paper proposes an estimator to provide precise position, velocity, and orientation for a landing aircraft relative to the runway based on image sensors, IMU sensor data, and barometric sensor measurements. This work excludes GPS data, as it is expected that the proposed estimator can be used for fault detection in GPS and instrument landing systems. The runway is unknown, and hence, its width is estimated online. An adaptive UKF-based approach is adopted to estimate the states. In order to verify the nearer-to-practical performance and accuracy of bias estimation, realistically simulated data that constitute noises and biases from the sensors are taken into consideration. The proposed approach provides a solution for nonlinear state estimation in the presence of measurement noises that are non-additive. In addition to this, it provides estimates of the unknown measurement noise covariance. A comparative study is conducted between the conventional UKF and the proposed AUKF to analyze the adopted approach. Results show that the proposed algorithm gives comparable estimation results and faster convergence than the conventional UKF.