ALGORITHMIC SUPPORT FOR INTEGRATED NAVIGATION AND INFORMATION SYSTEMS ON CONTAINER SHIPS TO IMPROVE OPERATIONAL EFFICIENCY

Abstract

The growth in transoceanic shipping volumes and the introduction of large- and ultra-large-capacity container ships ULCVs have led to increased demands for navigational accuracy, energy efficiency, and sound management decisions in the face of the uncertainties of the maritime environment. Traditional approaches to navigation, based on the use of isolated data sources and deterministic models, do not provide an adequate level of adaptability to changing external influences and limit the possibilities for optimizing ship operational parameters. The aim of this work is to develop algorithmic support for integrated navigation and information systems for container ships based on big data processing, mathematical modeling of ship motion, and methods of intelligent information processing. The study proposes an approach that combines data from global navigation satellite systems GNSS, hydroacoustic measurement devices, and meteorological observations using adaptive filtering methods, in particular Kalman filters, to form a consistent ship state vector. A mathematical model of ship motion based on the 3DOF approach has been refined to account for the effects of waves, wind, and currents, supplemented by correction terms generated through real-time data assimilation. An algorithm for integrated processing of navigation data and decision support is proposed, which enables ship trajectory prediction and route optimization based on multi-criteria indicators such as fuel consumption, voyage duration, and navigation safety. The results obtained demonstrate an increase in the accuracy of motion parameter estimation and trajectory prediction, as well as a reduction in operating costs due to a more rational selection of motion modes. It is shown that the proposed approach is universal and can be applied to container ships of various classes, in particular ULCVs, considering their inertial characteristics. The practical value of this work lies in the possibility of integrating the developed algorithmic software into modern navigation systems and digital maritime transport management platforms, which creates prerequisites for the further development of intelligent and autonomous navigation technologies