Print Email Facebook Twitter ADS-B Based Trajectory Prediction for Aerial Vehicles Title ADS-B Based Trajectory Prediction for Aerial Vehicles Author Wang, Haobo (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Rajan, R.T. (mentor) Li, Z. (mentor) van der Veen, A.J. (graduation committee) Sun, Junzi (graduation committee) Degree granting institution Delft University of Technology Programme Electrical Engineering | Signals and Systems Date 2023-10-27 Abstract The evolution of aerial vehicle technology necessitates robust trajectory prediction models. These models are crucial for maintaining safe airspace and enabling autonomous operations. Automatic dependent surveillance–broadcast (ADS-B) is a surveillance system that enables aircraft to receive data from navigation satellites and periodically broadcasts it, enabling it to be tracked. Moreover, using ADS-B data for more general aerial vehicles has become a popular trend because it can provide real-time high-resolution aircraft state information and share this information with other vehicles in real-time for the aviation safety ecosystem.In this project, we delve into ADS-B-based trajectory prediction for both aircraft and drone motion trajectories with the overarching goal of improving prediction accuracy. We initially implement several model-based Kalman filters—including interactive multiple models (IMM)—to assess the accuracy of aircraft trajectory predictions across different model structures. The results reveal that the IMM filter outperforms the single model predictions in terms of root mean square error (RMSE).Furthermore, we implement the Gaussian process (GP) with a sliding window scheme to predict online drone trajectories. Recognizing the high computational complexity of the GP, we also introduce a low-rank approximation method, structured kernel interpolation (SKI) GP, aiming to conserve computational resources. Finally, we compare the prediction performances of the IMM filter, classical GP, and SKI GP on real drone trajectories. The results highlight that the classical GP method enhanced prediction accuracy, achieving an RMSE of less than 1.7m, which is 50% lower compared to the model-based IMM filter. Additionally, the SKI GP realizes a 25% reduction in computation time compared to the classical GP, despite a slight compromise in prediction accuracy. Subject ADS-BInteractive Multiple Model FilterGaussian ProcessTrajectory prediction To reference this document use: http://resolver.tudelft.nl/uuid:d3530aec-8c39-4f65-94ca-f59f3c6de267 Part of collection Student theses Document type master thesis Rights © 2023 Haobo Wang Files PDF Thesis_Haobo_Wang.pdf 5.34 MB Close viewer /islandora/object/uuid:d3530aec-8c39-4f65-94ca-f59f3c6de267/datastream/OBJ/view