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Big Data Driven Vessel Trajectory and Navigating State Prediction With Adaptive Learning, Motion Modeling and Particle Filtering Techniques

https://doi.org/10.1109/tits.2020.3040268

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Outgoing claims: the papers this work cites, builds on, or argues with.

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Theory and Application of Artificial Neural Networks for the Real Time Prediction of Ship Motion

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Study on collision avoidance in busy waterways by using AIS data

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A novel ship trajectory reconstruction approach using AIS data

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Associative Learning of Vessel Motion Patterns for Maritime Situation Awareness

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Probabilistic associative learning of vessel motion patterns at multiple spatial scales for maritime situation awareness

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SAR Ship Detection and Self-Reporting Data Fusion Based on Traffic Knowledge

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Use of the Automatic Identification System (AIS) for maritime domain awareness (MDA)

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Knowledge-based vessel position prediction using historical AIS data

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Ship Surveillance With TerraSAR-X

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Maritime Traffic Monitoring Based on Vessel Detection, Tracking, State Estimation, and Trajectory Prediction

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Maritime Traffic Probabilistic Forecasting Based on Vessels’ Waterway Patterns and Motion Behaviors

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Traffic Pattern Mining and Forecasting Technologies in Maritime Traffic Service Networks: A Comprehensive Survey

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Concurrent Processing Cluster Design to Empower Simultaneous Prediction for Hundreds of Vessels' Trajectories in Near Real-Time

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The Ship Movement Trajectory Prediction Algorithm Using Navigational Data Fusion

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A Novel Approach for Ship Trajectory Online Prediction Using BP Neural Network Algorithm

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