Navigation Attitude Prediction for Unmanned Surface Vessels in Wave Environments Using Improved Unscented Kalman Filter and Digital Twin Model

Unmanned surface vehicles (USVs) face significant challenges in long-term operations in complex and dynamic marine environments. These include abnormal attitudes, low accuracy in navigation attitude prediction, and difficulties in maintaining operational stability and equipment safety. To address these issues, this paper proposed a USV navigation attitude prediction method that integrates Unscented Kalman Filtering (UKF) with a digital twin model. First, a three-degree-of-freedom mathematical model is constructed based on the motion characteristics of the USV to establish an initial digital twin model. Then, the UKF algorithm is improved with a dynamic sliding window approach and integrated with real vessel experimental data to achieve dynamic model parameter updates, further enhancing prediction accuracy. The updated twin model is subsequently used for USV navigation attitude prediction. Experimental results demonstrate that this method significantly improves prediction accuracy and robustness, even under complex sea conditions and sensor data loss, providing crucial support for the safety and reliability of USV autonomous navigation.