Multi-horizon flight trajectory prediction enabled by time-frequency wavelet transform
Dongyue Guo, Zheng Zhang, Jiayi Liu, Jianwei Zhang, Yi Lin
Abstract
Flight trajectory prediction is a fundamental task in air traffic control. While our previous WTFTP framework leveraged wavelet-based time-frequency analysis for trajectory prediction, its iterative single-horizon paradigm suffers from error accumulation in long-horizon forecasts. Here, the WTFTP+ framework is proposed to enhance multi-horizon prediction performance while further exploring the potential of time-frequency analysis in flight trajectory prediction tasks. An encoder-decoder neural architecture is designed to generate wavelet components of predicted trajectories, where a direct multi-horizon prediction paradigm is employed to mitigate the cumulative errors of the WTFTP. Additionally, a time-frequency bridging mechanism is proposed to explore intrinsic correlations among multi-scale flight patterns to enhance the learning ability of wavelet components. Experiments on real-world datasets demonstrate that WTFTP+ not only retains the superior single-horizon prediction performance of WTFTP but also significantly improves multi-horizon prediction accuracy, achieving over 40% mean deviation error reduction at the 5-minute horizon compared with WTFTP. The paper presents an enhanced framework for predicting flight paths using wavelet transform. It improves accuracy by addressing error accumulation and capturing multi scale flight patterns, achieving significant error reduction in multi-horizon predictions compared to previous methods.