Maritime Traffic Monitoring Based on Vessel Detection, Tracking, State Estimation, and Trajectory Prediction

• Published in: IEEE transactions on intelligent transportation systems Vol. 13; no. 3; pp. 1188 - 1200
• Main Authors: Perera, Lokukaluge P., Oliveira, Paulo, Guedes Soares, C.
• Format: Journal Article
• Language: English
• Published: IEEE 01.09.2012
• Subjects: Artificial neural networks; Extended Kalman filter (EKF); Kalman filters; Marine vehicles; Monitoring; neural networks; Radar tracking; Sensors; ship detecting and tracking; ship navigational trajectory prediction; State estimation; Trajectory; vessel state estimation (VSE); vessel traffic monitoring and information system (VTMIS)
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2012.2187282

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.