VMD-NGO-BiLSTM-based attitude angle prediction for unmanned vessels

Yuze Hang; Zhilin Zhang

doi:10.1117/12.3050638

27 September 2024 VMD-NGO-BiLSTM-based attitude angle prediction for unmanned vessels

Yuze Hang, Zhilin Zhang

Proceedings Volume 13281, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2024); 132810M (2024) https://doi.org/10.1117/12.3050638
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning, 2024, Zhengzhou, China

Abstract

Accompanied by the progress and development of artificial intelligence technology, the surface unmanned ship plays a key role in the unmanned control system, and plays a key role in data acquisition and hydrological survey. In the process of practical application, the stable operation of the unmanned ship will be greatly affected because of the complex operation and the unknown environment, so it is very necessary to predict its bits. Accurately predicting the movement of unmanned vessels is critical for navigation, safety monitoring and mission planning, and can significantly improve their stability and operational efficiency in complex waters. This paper introduces the variational modal decomposition (VMD) algorithm and proposes a combined prediction model (VMD-NGO-LSTM) based on the Northern Goshawk Optimisation Algorithm (NGO) and Bidirectional Long and Short-Term Storage Memory Network (BiLSTM), which is found to be highly accurate by processing the prediction of unmanned boat navigation data in Xiyuan River. It reflects a faster training speed when processing large unmanned boat data, and has a certain reference value for the position adjustment of unmanned boats.

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

Citation Download Citation

Yuze Hang and Zhilin Zhang "VMD-NGO-BiLSTM-based attitude angle prediction for unmanned vessels", Proc. SPIE 13281, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2024), 132810M (27 September 2024); https://doi.org/10.1117/12.3050638

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