Flight trajectory prediction enabled by time-frequency wavelet transform.

ABSTRACT: Accurate flight trajectory prediction is a crucial and challenging task in air traffic control, especially for maneuver operations. Modern data-driven methods are typically formulated as a time series forecasting task and fail to retain high accuracy. Meantime, as the primary modeling method for time series forecasting, frequency-domain analysis is underutilized in the flight trajectory prediction task. In this work, an innovative wavelet transform-based framework is proposed to perform time-frequency analysis of flight patterns to support trajectory forecasting. An encoder-decoder neural architecture is developed to estimate wavelet components, focusing on the effective modeling of global flight trends and local motion details. A real-world dataset is constructed to validate the proposed

SUBMITTER: Zhang Z 

PROVIDER: S-EPMC10465572 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature