Poor recognition and prediction of near miss collision risk can lead to catastrophic safety incidents for maritime safety engineering and pollution preparedness. Several methods have been proposed to support decision making related to ship-ship collision risk prediction. Most of them are mainly intended to analyze and predict one or more ship navigational parameters. This points to an urgent need for improvement of existing methods: as reasonable collision risk prediction of the next few times are necessary for decision making, this relies on optimal navigation data and accurate prediction algorithm. In this paper, the Extended Kalman Filter method is introduced, which provide the next optimal navigation data for ship-ship near miss collision risk analysis. Thereafter, according to the collision risk level, the near miss for the next two moments can be predicted by means of Unscented Kalman Filter method. The results indicate that the present work can accurately classify the collision risk level for two-vessel encounters and mitigate the human judgment error.

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