Research on the improved LSTM for target trajectory prediction

Zheng Xu; Yuan Tan; Guangjun Zeng; Kebin Chen; Fen Tang

doi:10.1117/12.3038334

7 August 2024 Research on the improved LSTM for target trajectory prediction

Zheng Xu, Yuan Tan, Guangjun Zeng, Kebin Chen, Fen Tang

Proceedings Volume 13229, Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024); 132292Q (2024) https://doi.org/10.1117/12.3038334
Event: Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024), 2024, Nanchang, China

Abstract

With the rapid development of modern traffic network, it is very important to accurately predict the trajectory of key traffic nodes. Aircraft navigation path, as a kind of data with complex temporal characteristics, is often affected by climate change and environmental disturbance, which makes the dynamic prediction of path characteristics a challenge. In this context, a novel trajectory prediction technique combining LSTM model is introduced in this study. The technique achieves accurate prediction of the target path through deep training of a large amount of data. The experimental results fully demonstrate the significant advantages of this method compared with traditional algorithms in the accuracy of flight target prediction and data processing efficiency.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zheng Xu, Yuan Tan, Guangjun Zeng, Kebin Chen, and Fen Tang "Research on the improved LSTM for target trajectory prediction", Proc. SPIE 13229, Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024), 132292Q (7 August 2024); https://doi.org/10.1117/12.3038334

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available