Maneuvering vessel detection and tracking (VDT), incorporated with state estimation and trajectory prediction, are important tasks for vessel navigational systems (VNSs), as well as vessel traffic monitoring and information systems (VTMISs) to improve maritime safety and security in ocean navigation. Although conventional VNSs and VTMISs are equipped with maritime surveillance systems for the same purpose, intelligent capabilities for vessel detection, tracking, state estimation, and navigational trajectory prediction are underdeveloped. Therefore, the integration of intelligent features into VTMISs is proposed in this paper. The first part of this paper is focused on detecting and tracking of a multiple-vessel situation. An artificial neural network (ANN) is proposed as the mechanism for detecting and tracking multiple vessels. In the second part of this paper, vessel state estimation and navigational trajectory prediction of a single-vessel situation are considered. An extended Kalman filter (EKF) is proposed for the estimation of vessel states and further used for the prediction of vessel trajectories. Finally, the proposed VTMIS is simulated, and successful simulation results are presented in this paper.

Experimental evaluations on autonomous navigation and collision avoidance of ship maneuvers by intelligent guidance are presented in this paper. These ship maneuvers are conducted on an experimental setup that consists of a navigation and control platform and a vessel model, in which the mathematical formulation presented is actually implemented. The mathematical formulation of the experimental setup is presented under three main sections: vessel traffic monitoring and information system, collision avoidance system, and vessel control system. The physical system of the experimental setup is presented under two main sections: vessel model and navigation and control platform. The vessel model consists of a scaled ship that has been used in this study. The navigation and control platform has been used to control the vessel model and that has been further divided under two sections: hardware structure and software architecture. Therefore, the physical system has been used to conduct ship maneuvers in autonomous navigation and collision avoidance experiments. Finally, several collision avoidance situations with two vessels are considered in this study. The vessel model is considered as the vessel (i.e., own vessel) that makes collision avoidance decisions/actions and the second vessel (i.e., target vessel) that does not take any collision avoidance actions is simulated. Finally, successful experimental results on several collision avoidance situations with two vessels are also presented in this study.