Aircraft Trajectory Prediction using ADS-B Data

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Automatic Dependent Surveillance - Broadcast (ADS-B) is a surveillance technology that is used extensively in Air Traffic Control (ATC) applications. Aircraft equipped with ADS-B transponders actively broadcast navigation information such as position, altitude, and velocity, and thus ATC is able to track aircraft continuously, even in regions not covered by traditional radars. However, raw ADS-B messages are typically contaminated with noise, which is typically mitigated using model-based tracking methods to predict the trajectories. In this work, we propose and evaluate the performance of several filtering strategies for trajectory prediction on an existing open source TrajAir aircraft data set and our own data set i.e., collected by Delft university of technology (TUD). In our evaluation, we observe the standard Kalman filter cannot accurately track the aircraft trajectory, especially for sharply maneuvering targets. A fading-memory filter tracks maneuvering targets but introduces delay in estimates, and requires a trade-off between responsiveness and smoothness by target-specific parameter tuning. The Kalman filter with augmented process noise also involves similar trade-off and parameter tuning. Finally, the particle filter performs the best during target maneuvers but admits more noise during steady-state and increases computational cost. In this paper, we present various filtering techniques, and study the performance of these algorithms on the TrajAir and TUD aircraft data sets.
Original languageEnglish
Title of host publication42nd WIC Symposium on Information Theory and Signal Processing in the Benelux (SITB 2022)
EditorsJérôme Louveaux, François Quitin
Number of pages10
Publication statusPublished - 2022
Event42nd WIC Symposium on Information Theory and Signal Processing in the Benelux - Louvain la Neuve, Belgium
Duration: 1 Jun 20222 Jun 2022
Conference number: 42


Conference42nd WIC Symposium on Information Theory and Signal Processing in the Benelux
Abbreviated titleSITB 2022
CityLouvain la Neuve


  • Filtering
  • ADS-B
  • Trajectory prediction
  • sensor fusion


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