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

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.Copyright © 2011 by ASME

Lokukaluge P. Perera | C. Guedes Soares | Paulo Oliveira

[1] Lokukaluge P. Perera,et al. Ocean Vessel Trajectory Estimation and Prediction Based on Extended Kalman Filter , 2010 .

[2] Martin T. Hagan,et al. Neural network design , 1995 .

[3] Lokukaluge P. Perera,et al. Laser Measurement System based maneuvering Target tracking formulated by Adaptive Competitive Neural Networks , 2010 .

[4] Arthur Gelb,et al. Applied Optimal Estimation , 1974 .

[5] C. Guedes Soares,et al. Fuzzy logic based decision making system for collision avoidance of ocean navigation under critical collision conditions , 2011 .

[6] James Llinas,et al. An introduction to multisensor data fusion , 1997, Proc. IEEE.

[7] R. A. Best,et al. A new model and efficient tracker for a target with curvilinear motion , 1997, IEEE Transactions on Aerospace and Electronic Systems.

[8] Thor I. Fossen,et al. Path following control system for a tanker ship model , 2007 .

[9] James Llinas,et al. An introduction to multi-sensor data fusion , 1998, ISCAS '98. Proceedings of the 1998 IEEE International Symposium on Circuits and Systems (Cat. No.98CH36187).