A hierarchical and progressive ship-feature-based method for berthing and unberthing situation awareness

Abstract

To ensure the safety of the ship and port, it is crucial to accurately perceive and analyze the ship’s motion behavior and spatiotemporal features during the berthing and unberthing process. This paper proposes a ship-feature-based hierarchical and progressive situation awareness method. In this method, a progressive perception framework, progressing layer by layer from the data layer to the behavioral layer and then to the global layer, is constructed. Firstly, the Kalman filter algorithm is employed to measurement key parameters during the berthing and unberthing process, thereby obtaining the ship’s motion information. Secondly, by integrating the social force model with the motion information, the interaction forces between the ship and the shoreline are calculated, and then the behavior features of the ship are identified. Thirdly, a dynamic buffer zone is constructed in the spatiotemporal domain, and an adaptive smoothing method is introduced to extract key time points and evolutionary patterns, thereby enabling comprehensive spatiotemporal modeling of the entire berthing and unberthing process. At last, the feasibility of the proposed method is verified through the berthing and unberthing experiments conducted in Dalian Port, China.