Unmanned boats, while navigating at sea, utilize active compensation systems to mitigate wave disturbances experienced by onboard instruments and equipment. However, there exists a lag in the measurement of unmanned boat attitudes, thus introducing unmanned boat motion attitude prediction to compensate for the lag in the signal acquisition process. This paper, based on the basic principles of waves, derives the disturbance patterns of waves on unmanned boats from the wave energy spectrum. Through simulation analysis of unmanned boat motion attitudes, motion attitude data is obtained, providing experimental data for subsequent work. A combined prediction model based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Particle Swarm Optimization (PSO), and Support Vector Machine (SVM) is designed to predict the motion attitude of unmanned boats. Simulation results validate its superior prediction accuracy compared to traditional prediction models. For example, in terms of mean absolute error, it improves by 17% compared to the EMD-PSO-SVM model.

基于波的基本原理，本文推导无人船上波浪扰动的干扰模式，通过模拟分析无人船运动姿态来获得运动姿态数据，设计了一种基于CEEMDAN、PSO和SVM的组合预测模型，通过仿真结果验证其相比传统预测模型具有更高的预测准确性，对于平均绝对误差而言，相比EMD-PSO-SVM模型提高了17%。

基于CEEMDAN-PSO-SVM的无人水面船动态姿态预测