Flight trajectory prediction enabled by time-frequency wavelet transform

Nature Communications

Article

Zheng Zhang¹  Jianwei Zhang²  Yi Lin²  Shizhong Zhou³  Dongyue Guo³ 

[1] College of Computer Science, Sichuan University, 610065, Chengdu, Sichuan, China;College of Computer Science, Sichuan University, 610065, Chengdu, Sichuan, China;National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University, 610065, Chengdu, Sichuan, China;National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University, 610065, Chengdu, Sichuan, China;

关键词: ;   

DOI  :  10.1038/s41467-023-40903-9

 received in 2023-04-20, accepted in 2023-08-14,  发布年份 2023

来源: Springer

【 摘 要 】

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 approach, and the experimental results demonstrate that the proposed framework exhibits higher accuracy than other comparative baselines, obtaining improved prediction performance in terms of four measurements, especially in the climb and descent phase with maneuver control. Most importantly, the time-frequency analysis is confirmed to be effective to achieve the flight trajectory prediction task.

【 授权许可】

CC BY   
© Springer Nature Limited 2023

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