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Area of Science:

  • Aerospace Engineering
  • Data Science
  • Signal Processing

Background:

  • Accurate flight trajectory prediction is vital for air traffic control, yet current data-driven methods struggle with precision, especially during maneuvers.
  • Frequency-domain analysis, a key time series forecasting tool, remains underutilized in flight trajectory prediction.

Purpose of the Study:

  • To propose an innovative wavelet transform-based framework for time-frequency analysis of flight patterns to improve trajectory forecasting.
  • To enhance the accuracy of flight trajectory prediction, particularly for aircraft undergoing maneuvers.

Main Methods:

  • Developed an encoder-decoder neural network architecture to estimate wavelet components.
  • Utilized time-frequency analysis to model both global flight trends and local motion details.
  • Constructed and validated the framework using a real-world flight dataset.

Main Results:

  • The proposed wavelet transform framework demonstrated superior accuracy compared to existing baseline methods.
  • Achieved improved prediction performance across four key metrics.
  • Showcased particular effectiveness in predicting trajectories during climb and descent phases involving maneuver control.

Conclusions:

  • Time-frequency analysis using wavelet transforms is an effective approach for flight trajectory prediction.
  • The developed framework offers a significant advancement in the accuracy and reliability of air traffic control systems.
  • The method shows promise for enhancing aviation safety and efficiency through precise trajectory forecasting.