Collision avoidance based on predictive probability using Kalman filter

Kim, Jae Hyun; Lee, Sun-Wook; Jin, Eun Seok

doi:10.1016/j.ijnaoe.2022.100438

Cited by 11 publications

(1 citation statement)

References 8 publications

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“…They often do not consider the uncertainty of ship positions and velocities. In [108], a ship-prediction probability collision0avoidance method is proposed based on the Kalman filter, which combines an Unscented Kalman Filter to predict the ship state and obtain the probability of ship positions. This information is then used to plan the optimal collision-avoidance route.…”

Section: Short-term Ship-motion Predictionmentioning

confidence: 99%

A Review on Motion Prediction for Intelligent Ship Navigation

Zhang,

Chu,

Liu

et al. 2024

JMSE

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In recent years, as intelligent ship-navigation technology has advanced, the challenge of accurately modeling and predicting the dynamic environment and motion status of ships has emerged as a prominent area of research. In response to the diverse time scales required for the prediction of ship motion, various methods for modeling ship navigation environments, ship motion, and ship traffic flow have been explored and analyzed. Additionally, these motion-prediction methods are applied for motion control, collision-avoidance planning, and route optimization. Key issues are summarized regarding ship-motion prediction, including online modeling of motion models, real ship validation, and consistency in modeling, optimization, and control. Future technology trends are predicted in mechanism-data fusion modeling, large-scale model, multi-objective motion prediction, etc.

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Section: Short-term Ship-motion Predictionmentioning

confidence: 99%