Math-data integrated prediction model for ship maneuvering motion

Dong, Qi; Wang, Weining; Song, Jialin; Hao, Lizhu; Liu, Shaoman; Han, Bing; Qu, Kai

doi:10.1016/j.oceaneng.2023.115255

Cited by 16 publications

(4 citation statements)

References 31 publications

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“…Xiao et al [31] designed a machine learning model employing physical information to build a gray box model (GBM) to predict the speed of ships crossing the ocean. Dong et al [23] introduced a new mathematical data integration prediction (MDIP) model for predicting the maneuvering movement of ships. The MDIP model, proposed using mathematical data integration, exhibits greater generalization ability and opens up new ways for predicting the maneuvering movement of ships.…”

Section: Machine Learning Methodsmentioning

confidence: 99%

“…However, the application scenarios of traditional methods depend on boundary conditions. Machine learning methods can improve prediction accuracy by creating complex mathematical models to simulate ship movements [22,23]. However, machine learning approaches need to collect a considerable amount of labeled data and the establishment of appropriate rules.…”

Section: Introductionmentioning

confidence: 99%

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An Adaptive Multimodal Data Vessel Trajectory Prediction Model Based on a Satellite Automatic Identification System and Environmental Data

Xiao,

Hu,

Liu

et al. 2024

JMSE

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Ship trajectory prediction is essential for ensuring safe route planning and to have advanced warning of the dangers at sea. With the development of deep learning, most of the current research has explored advanced prediction methods based on historical spatio-temporal Automatic Identification System (AIS) data. However, environmental factors such as sea wind and visibility also affect ship navigation in real-world maritime shipping. Therefore, developing reliable models utilizing multimodal data, such as AIS and environmental data, is challenging. In this research, we design an adaptive multimodal vessel trajectory data prediction model (termed AMD) based on satellite AIS and environmental data. The AMD model mainly consists of an AIS-based extraction network, an environmental-based extraction network, and a fusion block. In particular, this work considers multimodal data such as historical spatio-temporal information and environmental factors. Time stamps and distances are correlated with AIS and environmental data, and a multilayer perceptron and gated recurrent unit networks are used to design multimodal feature extraction networks. Finally, the fusion block realizes the fusion output of multimodal features to improve the reliability of the AMD model. Several quantitative and qualitative experiments are conducted using real-world AIS and multimodal environmental datasets. Numerous experimental results prove that prediction performance using multimodal data can ensure satisfactory accuracy and reliability while exhibiting a positive impact on improving maritime transport services.

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Section: Machine Learning Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Adaptive Multimodal Data Vessel Trajectory Prediction Model Based on a Satellite Automatic Identification System and Environmental Data

Xiao,

Hu,

Liu

et al. 2024

JMSE

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show abstract

“…C. Gingrass et al proposed classifying flight patterns into nine categories using a deep sequential neural network from flight path data based on automatic dependent surveillance broadcast (ADS-B) [32]. There have been similar attempts targeting ships rather than aircraft; Q. Dong et al introduced the Mathematical-Data Integrated Prediction (MDIP) model for predicting ship maneuvers, blending mathematical forecasting using an extended Kalman filter with data-driven predictions via least squares support vector machines, showcasing improved generalization in various maneuvering tests [33]. N. Wang et al presented SeaBil, a deep-learning-based system using a self-attention-weighted Bi-LSTM and Conv-1D network for ultrashort-term prediction of ship maneuvering, demonstrating higher accuracy in predicting various ship motion states compared to other methods [34].…”

Section: Related Workmentioning

confidence: 99%

STEAM: Spatial Trajectory Enhanced Attention Mechanism for Abnormal UAV Trajectory Detection

Yoon,

Lee,

Kim

et al. 2023

Applied Sciences

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Accurate unmanned aerial vehicle (UAV) trajectory tracking is crucial for the successful execution of UAV missions. Traditional global positioning system (GPS) methods face limitations in complex environments, and visual observation becomes challenging with distance and in low-light conditions. To address this challenge, we propose a comprehensive framework for UAV trajectory verification, integrating a range-based ultra-wideband (UWB) positioning system and advanced image processing technologies. Our key contribution is the development of the Spatial Trajectory Enhanced Attention Mechanism (STEAM), a novel attention module specifically designed for analyzing and classifying UAV trajectory patterns. This system enables real-time UAV trajectory tracking and classification, facilitating swift and accurate assessment of adherence to predefined optimal trajectories. Another major contribution of our work is the integration of a UWB system for precise UAV location tracking, complemented by our advanced image processing approach that includes a deep neural network (DNN) for interpolating missing data from images, thereby significantly enhancing the model’s ability to detect abnormal maneuvers. Our experimental results demonstrate the effectiveness of the proposed framework in UAV trajectory tracking, showcasing its robust performance irrespective of raw data quality. Furthermore, we validate the framework’s performance using a lightweight learning model, emphasizing both its computational efficiency and exceptional classification accuracy.

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“…The obtained models were used to simulate the typical zig-zag manoeuvres with moderate and extreme steering. Dong et al [51] proposed a math-data integrated prediction (MDIP) model for ship manoeuvring motion, where the variable-order hydrodynamic derivatives were used. The results show that the proposed model can offer a stronger generalization, and possibly be used for the ship manoeuvring motion prediction.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Parameter Estimation of Nonlinear Ship Manoeuvring Model in Shallow Water Using Truncated Least Squares Support Vector Machines

Xu,

Guedes Soares

2023

JMSE

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A data-driven method, the truncated LS-SVM, is proposed for estimating the nondimensional hydrodynamic coefficients of a nonlinear manoeuvring model. Experimental data collected in a shallow water towing tank are utilized in this study. To assess the accuracy and robustness of the truncated LS-SVM method, different test data sizes are selected as the training set. The identified nondimensional hydrodynamic coefficients are presented, as well as the corresponding parameter uncertainty and confidence intervals. The validation is carried out using the reference data, and statistical measures, such as the correlation coefficient, centred RMS difference, and standard deviation are employed to quantify the similarity. The results demonstrate that the truncated LS-SVM method effectively models the hydrodynamic force prediction problems with a large training set, reducing parameter uncertainty and yielding more convincing results.

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Math-data integrated prediction model for ship maneuvering motion

Cited by 16 publications

References 31 publications

An Adaptive Multimodal Data Vessel Trajectory Prediction Model Based on a Satellite Automatic Identification System and Environmental Data

An Adaptive Multimodal Data Vessel Trajectory Prediction Model Based on a Satellite Automatic Identification System and Environmental Data

STEAM: Spatial Trajectory Enhanced Attention Mechanism for Abnormal UAV Trajectory Detection

Data-Driven Parameter Estimation of Nonlinear Ship Manoeuvring Model in Shallow Water Using Truncated Least Squares Support Vector Machines

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