【摘要】 为提高舰载靶机发射过程中船舶运动姿态的预测精度，使用基于长短期记忆（Long short-term memory,LSTM）网络的船舶姿态预测方法。针对长时预测导致的误差累计问题，提出了改进窗口滑动法，通过对每次预测结果进行变分模态分解（Variational mode decomposition, VMD）滤波，消除累积误差引起的预测结果振荡。通过有限元仿真及自主设计的船模实验平台开展波浪水池试验，采集横摇、纵摇、垂荡等关键姿态参数的时序数据。实验设置涵盖1级至5级典型海况条件。实验结果表明，该模型在升沉位移、横摇角及纵摇角预测中，均方误差（Mean squared error, MSE）最大降幅可达99.4%,MAPE降低至2.11%，验证了其工程应用的有效性。研究成果可为舰载靶机发射引导系统提供高精度的船舶运动态势预判，对提升着舰安全性具有重要工程价值。更多还原

【Abstract】 In order to improve the prediction accuracy of the ship’s motion attitude during the launch of the carrier-based target drone, this paper uses a ship attitude prediction method based on the long short-term memory(LSTM) network. In view of the error accumulation problem caused by long-term prediction, this paper proposes an improved window sliding method, which eliminates the prediction result oscillation caused by the cumulative error by filtering each prediction result with variational mode decomposition(VMD). The wave tank test was carried out through finite element simulation and a self-designed ship model experimental platform to collect time series data of key attitude parameters such as roll, pitch, and heave. The experimental setting covers typical sea conditions from level 1 to level 5. The experiment shows that the maximum mean squared error(MSE) reduction of the model in the prediction of heave displacement, roll angle and pitch angle is 99.4%, and the MAPE is reduced to 2.11%, which verifies the effectiveness of its engineering application. The research results can provide high-precision ship motion situation prediction for the launch guidance system of the carrier-based target drone, which has important engineering value for improving landing safety.更多还原