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

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.