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Radar and Automatic Identification System Track Fusion in an Electronic Chart Display and Information System

Published online by Cambridge University Press:  04 June 2015

Witold Kazimierski  and
Andrzej Stateczny
Witold Kazimierski*
Affiliation:
(Institute of Geoinformatics, Faculty of Navigation, Maritime University of Szczecin, Poland)
Andrzej Stateczny
Affiliation:
(Marine Technology Ltd., Szczecin, Poland)
*
(E-mail: [email protected])
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Abstract

This paper presents the results of research on the fusion of tracking radar and an Automatic Identification System (AIS) in an Electronic Chart Display and Information System (ECDIS). First, the concept of these systems according to the International Maritime Organization (IMO) is described, then a set of theoretical information on radar tracking and the fusion method itself is given and finally numerical results with real data are presented. Two methods of fusion, together with their parameters, are examined. A proposal for calculating the covariance matrix for radar and AIS data is also given, and the paper ends with conclusions.

Keywords

RadarAutomatic Identification SystemIntegrated navigationECDIS
Type
Research Article
Information
The Journal of Navigation , Volume 68 , Issue 6 , November 2015 , pp. 1141 - 1154
Copyright
Copyright © The Royal Institute of Navigation 2015 

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References

REFERENCES

Adamski, M. E., Kulpa, K. S., Nalecz, M. and Wojtkiewicz, A. (2000). Phase Noise in Two-dimensional Spectrum of Video Signal in FMCW Homodyne Radar. Proceedings of 13th International Conference on Microwaves, Radar and Wireless Communications (MIKON-2000), vol. 2, 645–648, Warsaw, Poland.CrossRefGoogle Scholar
Borkowski, P. (2012). Data Fusion in a Navigational Decision Support System on a Sea-going Vessel. Polish Maritime Research, 19, 78–85.CrossRefGoogle Scholar
Borkowski, P. and Zwierzewicz, Z. (2011). Ship Course-keeping Algorithm Based on Knowledge Base. Intelligent Automation and Soft Computing, 17, 149–163.CrossRefGoogle Scholar
Gan, Q. and Harris, C. J. (2001). Comparison of Two Measurement Fusion Methods for Kalman Filter Based Multisensor Data Fusion. IEEE Transactions on Aerospace and Electronic Systems, 37, 1, 273–280.CrossRefGoogle Scholar
Hansen, M., Gamborg, J., Toke, K., Lehn-Schioler, T., Melchild, K., Rasmussen, F. M. and Ennemark, F. (2013). Empirical Ship Domain based on AIS Data. The Journal of Navigation, 66, 6, 931–940.CrossRefGoogle Scholar
Hill, K., Sabol, C. and Alfriend, K.T. (2010). Comparison of Covariance-based Track Association Approaches with Simulated Radar Data. Proceedings of the Astrodynamics Symposium, AAS 10–318, Alfriend, K. T. (Ed.), Monterey, CA.Google Scholar
Hsu, H. Z., Witt, N. A., Hooper, J. B. and McDermott, A. P. (2009). The AIS-Assisted Collision Avoidance. The Journal of Navigation, 62, 4, 657–670.CrossRefGoogle Scholar
International Electrotechnical Commission (IEC) (2008). IEC 61174 Maritime Navigation and Radiocommunication Equipment and Systems – Electronic Chart Display and Information System (ECDIS) – Operational and Performance Requirements, Methods of Testing and Required Test Results, Ed. 3·0. Geneva, Switzerland.Google Scholar
International Electrotechnical Commission (IEC) (2013). IEC 62388 Maritime Navigation and Radiocommunication Equipment and Systems – Shipborne Radar – Performance Requirements, Methods of Testing and Required Test Results, Ed. 2·0. Geneva, Switzerland.Google Scholar
International Maritime Organization (IMO) (2000). Regulation 19 of SOLAS, Chapter V - Carriage Requirements for Shipborne Navigational Systems and Equipment.Google Scholar
International Maritime Organization (IMO) (2004). Resolution MSC.192(79) – Adoption of the Revised Performance Standards for Radar Equipment.Google Scholar
International Maritime Organization (IMO) (2006). Resolution MSC.232(82) – Adoption of the Revised Performance Standards for Electronic Chart Display and Information Systems (ECDIS).Google Scholar
Kazimierski, W. (2013). Problems of Data Fusion of Tracking Radar and AIS for the Needs of Integrated Navigation Systems at Sea. Proceedings of IRS, Rohling, H. (Ed.), 270–275, Dresden, Germany.Google Scholar
Kazimierski, W. and Stateczny, A. (2011). Multisensor Tracking of Marine Targets – Decentralized Fusion of Kalman and Neural Filters. International Journal of Electronics and Telecommunications, 57, 65–70.Google Scholar
Kazimierski, W. and Stateczny, A. (2012). Optimization of Multiple Model Neural Tracking Filter for Marine Targets. Proceedings of 13th International Radar Symposium (IRS). Book Series: International Radar Symposium Proceedings, Kulpa, K. (Ed.), 543–548, Warsaw, Poland.Google Scholar
Kazimierski, W. and Wawrzyniak, N. (2014). Exchange of Navigational Information between VTS and RIS for Inland Shipping User Needs. Telematics in the Transport Environment, J. Mikulski, J. (Ed.), Communications in Computer and Information Science 471, 294–303, Ustron, Poland.Google Scholar
Kazimierski, W. and Zaniewicz, G. (2014). Analysis of the Possibility of Using Radar Tracking Method Based on GRNN for Processing Sonar Spatial Data. Proceedings of 2014 Joint Rough Set Symposium, Lecture Notes in Artificial Intelligence, Kryszkiewicz, et al. (Eds), 319–326, Granada-Madrid, Spain.CrossRefGoogle Scholar
Kazimierski, W., Zaniewicz, G. and Stateczny, A. (2012). Verification of Multiple Model Neural Tracking Filter with Ship's Radar. Proceedings of 13th International Radar Symposium (IRS), International Radar Symposium Proceedings, Kulpa, K. (Ed.), 549–553, Warsaw, Poland.Google Scholar
Kulpa, K. S. (2001). Novel Method of Decreasing Influence of Phase Noise on FMCW Radar. Proceedings of CIE International Conference on Radar, 319–323, Beijing, China.CrossRefGoogle Scholar
Kulpa, K. S., (2003). Focusing Range Image in VCO Based FMCW Radar. Proceedings of Radar Conference, 235–238, Adelaide, USA.CrossRefGoogle Scholar
Kulpa, K. S., Wojtkiewicz, A., Nalecz, M. and Misiurewicz, J. (2000). The Simple Method for Analysis of Nonlinear Frequency Distortions in FMCW Radar. Proceedings of 13th International Conference on Microwaves, Radar and Wireless Communications (MIKON-2000), vol. 1, 235–238, Warsaw, Poland.CrossRefGoogle Scholar
Kwiatkowski, M., Popik, J., Buszka, W. and Wawruch, R. (2011). Integrated Vessel Traffic Control System, Transport Systems and Processes – Marine Navigation and Safety of Sea Transportation. Weintrit, & Neumann, (Eds), CRC Press.Google Scholar
Last, P., Bahlke, C., Hering-Bertram, M., and Linsen, L. (2014). Comprehensive Analysis of Automatic Identification System (AIS) Data in Regard to Vessel Movement Prediction. The Journal of Navigation, 67, 5, 791–809.CrossRefGoogle Scholar
Liggins, M. E., Llinas, J. and Hall, D. L. (2009). Handbook of Multisensor Data Fusion: Theory and Practice, Second Edition (Electrical Engineering & Applied Signal Processing Series). CRC Press.Google Scholar
Lubczonek, J. (2004). Hybrid Neural Model of the Sea Bottom Surface, Artificial Intelligence and Soft Computing – ICAISC 2004, Lecture Notes in Artificial Intelligence, Rutkowski, L., Siekmann, J., Tadeusiewicz, R., et al. (Eds), vol. 3070, 1154–1160, Zakopane, Poland.Google Scholar
Lubczonek, J. (2008). Application of GIS Techniques in VTS Radar Stations Planning, Proceedings of IRS, Kawalec, A. and Kaniewski, P. (Eds), 277–280, Wroclaw, Poland.Google Scholar
Lubczonek, J. and Stateczny, A. (2003). Concept of Neural Model of the Sea Bottom Surface, Neural Networks and Soft Computing Book Series: Advances in Soft Computing, Rutkowski, L. and Kacprzyk, J. (Eds), 861–866, Zakopane, Poland.Google Scholar
Lubczonek, J. and Stateczny, A. (2009). Aspects of Spatial Planning of Radar Sensor Network for Inland Waterways Surveillance. Proceedings of 6th European Radar Conference (EURAD 2009), European Radar Conference-EuRAD, 501–504, Rome, Italy.Google Scholar
Malleswaran, M., Vaidehi, V., Irwin, S. and Robin, B. (2013). IMM-UKF-TFS Model-based Approach for Intelligent Navigation. The Journal of Navigation, 66, 6, 859–877.CrossRefGoogle Scholar
Matzka, S. and Altendorfer, R. (2008). A Comparison of Track-to-track Fusion Algorithms for Automotive Sensor Fusion. Proceedings of IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, Seoul, South Korea.CrossRefGoogle Scholar
Motwani, A., Sharma, S. K., Sutton, R. and Culverhouse, P. (2013). Interval Kalman Filtering in Navigation System Design for an Uninhabited Surface Vehicle. The Journal of Navigation, 66, 5, 639–652.CrossRefGoogle Scholar
Mou, J. M., van der Tak, C. and Ligteringen, H. (2010). Study on Collision Avoidance in Busy Waterways by using AIS Data. Ocean Engineering, 37, 5–6, 483–490.CrossRefGoogle Scholar
Pietrzykowski, Z., Borkowski, P. and Wolejsza, P. (2012). Marine Integrated Navigational Decision Support System, Telematics in the Transport Environment, Communications in Computer and Information Science, Mikulski, J. (Ed.), vol. 329, 284–292, Ustron, Poland.Google Scholar
Silveira, P. A. M., Teixeira, A. P. and Guedes Soares, C. (2013). Use of AIS Data to Characterise Marine Traffic Patterns and Ship Collision Risk off the Coast of Portugal. The Journal of Navigation, 66, 6, 879–898.CrossRefGoogle Scholar
Stateczny, A. (2000). The Neural Method of Sea Bottom Shape Modelling for the Spatial Maritime Information System, Maritime Engineering and Ports II, Water Studies Series, Brebbia, C. A. and Olivella, J. (Eds), vol. 9, 251–259, Barcelona, Spain.Google Scholar
Stateczny, A. (2002a). Methods of Comparative Plotting of the Ship's Position, Maritime Engineering & Ports III, Water Studies Series, Brebbia, C. A. and Sciutto, G. (Eds), vol. 12, 61–68, Rhodes, Greece.Google Scholar
Stateczny, A. (2002b). Neural Manoeuvre Detection of the Tracked Target in ARPA Systems, Control Applications in Marine Systems 2001 (CAMS 2001), IFAC Proceedings Series, Katebi, R. (Ed.), 209–214, Glasgow, Scotland.CrossRefGoogle Scholar
Stateczny, A. (2004). Artificial Neural Networks for Comparative Navigation, Artificial Intelligence and Soft Computing – ICAISC 2004, Lecture Notes in Artificial Intelligence, Rutkowski, L., Siekmann, J., Tadeusiewicz, R., et al. (Eds), vol. 3070, 1187–1192, Zakopane, Poland.Google Scholar
Stateczny, A. and Bodus-Olkowska, I. (2014). Hierarchical Hydrographic Data Fusion for Precise Port Electronic Navigational Chart Production, Telematics in the Transport Environment, Communications in Computer and Information Science, Mikulski, J. (Ed.), vol. 471, 359–368, Ustron, Poland.Google Scholar
Stateczny, A. and Kazimierski, W. (2006). Selection of GRNN Network Parameters for the Needs of State Vector Estimation of Manoeuvring Target in ARPA Devices, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments IV, Proceedings of the Society of Photo-Optical Instrumentation Engineers (SPIE), Romaniuk, R. S. (Ed.), vol. 6159, F1591–F1591, Wilga, Poland.Google Scholar
Stateczny, A. and Kazimierski, W. (2008a). A Comparison of the Target Tracking in Marine Navigational Radars by Means of GRNN Filter and Numerical Filter. Proceedings of 2008 IEEE Radar Conference, vols 1–4, 1994–1997, Rome, Italy.CrossRefGoogle Scholar
Stateczny, A. and Kazimierski, W. (2008b). Determining Manoeuvre Detection Threshold of GRNN Filter in the Process of Tracking in Marine Navigational Radars. Proceedings of International Radar Symposium, Kawalec, A. and Kaniewski, P. (Eds), 242–245, Wroclaw, Poland.CrossRefGoogle Scholar
Stateczny, A. and Kazimierski, W. (2013). Sensor Data Fusion in Inland Navigation. Proceedings of IRS, Rohling, H. (Ed.), 264–269, Dresden, Germany.Google Scholar
Stateczny, A. and Lubczonek, J. (2014). Radar Sensors Implementation in River Information Services in Poland. Proceedings of 15th International Radar Symposium (IRS), Kulpa, K. (Ed.), 199–203, Gdansk, Poland.CrossRefGoogle Scholar
Stateczny, A. and Wlodarczyk-Sielicka, M. (2014). Self-Organizing Artificial Neural Networks into Hydrographic Big Data Reduction Process. Proceedings of Joint Rough Set Symposium, Lecture Notes in Artificial Intelligence, Kryszkiewicz, et al. (Eds), 335–342, Granada-Madrid, Spain.CrossRefGoogle Scholar
Tuzlukov, V. (2013). Signal Processing in Radar Systems, CRC Press, Taylor & Francis Group.Google Scholar
Wang, Y., Zhang, JF., Chen, QX., Chu, XM. and Yan, XP. (2013a). A Spatial-temporal Forensic Analysis for Inland-water Ship Collisions using AIS Data. Safety Science, 57, 187–202.CrossRefGoogle Scholar
Wang, Y., Zheng, W., An, X., Sun, SM. and Li, L. (2013b). XNAV/CNS Integrated Navigation Based on Improved Kinematic and Static Filter. The Journal of Navigation, 66, 6, 899–918.CrossRefGoogle Scholar
Wawrzyniak, N. and Hyla, T. (2014). Managing Depth Information Uncertainty in Inland Mobile Navigation Systems. Proceedings of Joint Rough Set Symposium, Lecture Notes in Artificial Intelligence, Kryszkiewicz, et al. (Eds), 343–350, Granada-Madrid, Spain.CrossRefGoogle Scholar
Yang, LJ., Cheng, YG., Wei, H. and Lu, JT. (2006). Error Analysis of Multi-Sensor Data Fusion System for Target Detection on the Ocean Surface. Proceedings of 2006 IEEE International Conference on Information Acquisition, vols 1 and 2, 415–419, Weihai, China.CrossRefGoogle Scholar
Zhao, Z., Ji, KF., Xing, XW., Zou, HX. and Zhou, SL. (2014). Ship Surveillance by Integration of Space-borne SAR and AIS – Further Research. The Journal of Navigation, 67, 2, 295–309.Google Scholar