M-Estimation as a Tool Supporting a Vessel Traffic Controller in the VTS System

Open access


In order to improve maritime safety and the efficiency of vessel traffic, systems supervising vessel traffic, i.e. VTS (Vessel Traffic Service), started to be created. These systems are aimed to control vessel traffic in waters where traffic congestion, a large concentration of vessels or the presence of navigational hazards creates a risk of collision or stranding.

VTS systems constitute maritime safety centres and they must be equipped with appropriate devices in order to be fully functional. Among devices that provide information about vessels are coastal radar stations which are located around a monitored sea area. This kind of spatial arrangement of these stations can be used to simultaneously obtain information about every vessel, but such observations may be fraught with serious errors. Therefore, the estimation methods that are employed and developed in geodesy can be used to improve the accuracy with which a vessel’s position is determined. The Interactive Navigational Structure, i.e. IANS, is an example of how these methods can be applied in navigation; this term has already been introduced into the literature (Czaplewski, 2004). The text below presents the theoretical assumptions underlying the use of IANS as a tool supporting a vessel traffic controller using the VTS system in his/her work. This presentation is supported by a numerical test that was performed in the waters of the Bay of Gdańsk which are covered by the VTS system.

1. Bole A.G., Wall A.W., Norris A., (2014). Radar and ARPA Manual, Third Edition: Radar, AIS and Target Tracking for Marine Radar Users 3rd Edition, Elsevier, ISBN 978-0-08-097752-2, Waltham, MA.

2. Borre K.J, Jorgensen P.C., Kubik K., (1983). Robust Adjustment of the Danish Fundamental Triangulation Network. Zeszyty Naukowe Akademii Górniczo - Hutniczej im. Stanisława Staszica, nr 79, s. 57 - 69, Kraków.

3. Czaplewski K. (2004). Positioning With Interactive Navigational Structures Implementation. Annual of Navigation no 7/2004, ISSN 1640-8632, Gdynia.

4. Hampel F.R, Ronchetti E.M, Rousseeuw P.J, Stahel W.A, (2005). Robust Statistics. The Approach Based on Influence Functions. John Wiley, ISBN 978-0-471-73577-9, New York, NJ

5. Jianjun Z. (1996). Robustness and the Robust Estimate. Journal of Geodesy, nr 70, s. 586 - 590, Heidelberg.

6. Monahan K. (2008) The Radar Book: Effective Navigation and Collision Avoidance, Fine Edge Productions, ISBN 978-1-932310-36-8, Anacortes, WA.

7. Teunissen P.J.G. (2004). Adjustment theory: an introduction (mathematical geodesy and positioning), Delft University Press, ISBN 978-90-407-1974-5, Delft.

8. Wiśniewski Z. (2004). Metody opracowania wyników pomiarów w nawigacji i hydrografii. AMW, ISBN 83-87280-65-8, Gdynia.

9. Wiśniewski Z. (2009). Rachunek wyrównawczy w geodezji (z przykładami). UWM, ISBN 83-7299-399-8, Olsztyn.

10. YangY., Song L., Xu T., (2002). Robust Estimator for Correlated Observations Based on Bifactor Equivalent Weights. Journal of Geodesy, nr 76, s. 353 - 358, Heidelberg.

Polish Maritime Research

The Journal of Gdansk University of Technology

Journal Information

IMPACT FACTOR 2017: 0.763
5-year IMPACT FACTOR: 0.816

CiteScore 2017: 0.99

SCImago Journal Rank (SJR) 2017: 0.280
Source Normalized Impact per Paper (SNIP) 2017: 0.788

Cited By


All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 88 88 7
PDF Downloads 65 65 6