Collaborated and Constrained Neural-EKF Algorithm for the Vessel Traffic Monitoring and Information System

Maneuvering vessel detection and tracking in cooperation with vessel state estimation and navigational trajectory prediction are important tasks for the Vessel Traffic Monitoring and Information Systems (VTMIS) to improve maritime safety and security in ocean navigation. In this study, collaborated and constrained Neural-EKF algorithm is proposed for the above purpose. The proposed methodology consists of two main units: an Artificial Neural Network based Vessel Detection and Tracking Unit and an Extended Kalman Filter based State Estimation and Trajectory Prediction Unit. Finally, the proposed algorithm, is implemented on the MATLAB software platform, and successfully illustrate the results attainable in respect to vessel detection and tracking, vessel state estimation and navigational trajectory prediction in ocean navigation is also presented in this study.