Influence of sample intervals in real-sea trails on the nonparametric model of 3-DoF ship motion predictions

• The advantages and disadvantages of selecting different sample intervals for data in real sea trails are discussed considering multiple aspects of data set features. • The frequency analysis of different datasets in the response domain was validated, showing that overly large sample intervals decrease the high-frequency component in the spectrum, while datasets with excessively small sample intervals contain excessive noise in the high-frequency part. • Real sea maneuverability test was performed, and the measured data was processed as the training set with varies control inputs. • Appropriate selection of sampling intervals that can significantly improve the precision in predicting ship motions is conducted. The accuracy of ship manoeuvrability models based on real sea trail data heavily relies on data quality, as noise and measurement errors pose significant challenges in data processing and model training. This study employs a deep learning approach for the nonparametric modelling of 3-DoF ship manoeuvrability predictions, utilizing data from zigzag tests conducted at sea. It examines how the limitations of measurement equipment affect data analysis, emphasizing the impact of different sample intervals on model accuracy. Our study further explores the efficacy of deep neural networks in capturing low-frequency components more effectively than high-frequency ones, discussing the data sampling process and frequency analysis in the response domain for training set construction. Simulation results indicate that both excessively small and large sample intervals can significantly compromise predictions of motion, location, and heading angles. Moreover, to enhance the evaluation of the deep learning-based nonparametric model, incorporating a test case with a minimal rudder angle is crucial for assessing the prediction precision. Generally, a sample interval ranging from 0.4 s to 0.6 s is identified as optimal for data down sampling in real sea trails, balancing accuracy and computational efficiency.