Hostname: page-component-78c5997874-j824f Total loading time: 0 Render date: 2024-11-11T08:49:21.575Z Has data issue: false hasContentIssue false
  • English
  • Français

A Statistical Model for Vessel-to-Vessel Distances to Evaluate Radar Interference

Published online by Cambridge University Press:  17 April 2017

Gaspare Galati Open the ORCID record for Gaspare Galati [Opens in a new window] ,
Gabriele Pavan Open the ORCID record for Gabriele Pavan [Opens in a new window] ,
Francesco De Palo  and
Giuseppe Ragonesi
Gaspare Galati
Affiliation:
(Department of Electronic Engineering, University of Rome Tor Vergata – Italy)
Gabriele Pavan*
Affiliation:
(Department of Electronic Engineering, University of Rome Tor Vergata – Italy)
Francesco De Palo
Affiliation:
(Department of Electronic Engineering, University of Rome Tor Vergata – Italy)
Giuseppe Ragonesi
Affiliation:
(Department of Electronic Engineering, University of Rome Tor Vergata – Italy)
*
(E-mail: gabriele.pavan@uniroma2.it)
Get access Rights & Permissions [Opens in a new window]

Abstract

Maritime traffic has significantly increased in recent decades due to its advantageous costs, delivery rate and environmental compatibility. With the advent of the new generation of marine radars, based on the solid-state transmitter technology that calls for much longer transmitted pulses, the interference problem can become critical. Knowing the positions and the heights of the ships, the mean number of the vessels in radar range can be estimated to evaluate the effects of their mutual radar interferences. This paper aims to estimate the probability density function of the mutual distances. The truncation of the density function within a limited area related to horizon visibility leads to a simple single-parameter expression, useful to classify the ships as either randomly distributed or following a defined route. Practical results have been obtained using Automatic Identification System (AIS) data provided by the Italian Coast Guard in the Mediterranean Sea.

Keywords

Marine RadarVessel-to-vessel distanceRadar interference
Type
Research Article
Information
The Journal of Navigation , Volume 70 , Issue 5 , September 2017 , pp. 1098 - 1116
Copyright
Copyright © The Royal Institute of Navigation 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Abramowitz, M. and Stegun, I.A. (1964). Handbook of mathematical functions. New York Dover Publications.Google Scholar
Amato, F., Fiorini, M., Gallone, S. and Golino, G. (2010). Fully solid state radar for vessel traffic services. 11 th International Radar Symposium (IRS), 2010.Google Scholar
Chang, S.J., Hsu, G.Y., Yang, J.A., Chen, K.N., Chiu, Y.F. and Chang, F.T. (2010). Vessel Traffic Analysis for Maritime Intelligent Transportation System. 2010 IEEE 71 st Vehicular Technology Conference (VTC 2010-Spring), 16–19 May, doi: 10.1109/VETECS.2010.5493942 Google Scholar
Galati, G. and Pavan, G. (2015). Mutual interference problems related to the evolution of marine radars. Proceedings of the Intelligent Transportation Systems (ITSC), Las Palmas, Gran Canaria (Spain), 1785–1790.Google Scholar
Galati, G., Pavan, G. and De Palo, F. (2015). Interference problems expected when solid-state marine radars will come into widespread use. Proceedings of the Eurocon 2015, Salamanca (Spain), 1–6.Google Scholar
Galati, G., Pavan, G. and De Palo, F. (2016). Compatibility problems related with pulse compression, solid-state marine radars. IET Journal Radar, Sonar & Navigation, 1–7, doi:10.1049/iet-rsn.2015.0400.Google Scholar
Gleser, L.J. and Moore, D.S. (1983). The effect of dependence on chi-squared and empiric distribution tests of fit. The Annals of Statistics, 11(4), 1100–1108.Google Scholar
Haiyan, W. and Youzhen, W. (2015). Vessel traffic flow forecasting with the combined model based on support vector machine. 2015 International Conference on Transportation Information and Safety (ICTIS), 25–28 June 2015, doi: 10.1109/ICTIS.2015.7232151 CrossRefGoogle Scholar
Hang, W., Xu, M., Chen, X. and Zhou, S. (2015) Vessel traffic flow prediction model based on complex network. 2015 International Conference on Transportation Information and Safety (ICTIS), 25–28 June 2015, doi: 10.1109/ICTIS.2015.7232104 Google Scholar
IMO (1997). Resolution A.857(20). Guidelines for Vessel Traffic Services.Google Scholar
Kraiman, J.B., Arouh, S.L. and Webb, M.L. (2001). Automated anomaly detection processor. In Proceedings of SPIE: Enabling Technologies for Simulation Science VI, Orlando, FL, USA, 1 April 2001; Sisti, A.F., Trevisani, D.A., Eds.; pp. 128–137.Google Scholar
Nelander, A. and Tòth-Pàl, Z. (2009). Modular System Design for a New S- Band Marine Radar. International Radar Conference – Surveillance for a Safer World, 12–16 October 2009, Bordeaux, France.Google Scholar
Nelder, J.A. and Mead, R. (1965). A simplex method for function minimization. The Computer Journal, 7(4), 308–313.Google Scholar
Papoulis, A. (1990). Probability and statistics. Prentice-Hall.Google Scholar
Philip, J. (1991). The probability distribution of the distance between two random points in a box. MSC: Primary 60D05, Secondary 52A22. Department of Mathematics, Royal Institute of Technology, Stockholm, Sweden.Google Scholar
Riveiro, M. (2011). Visual Analytics for Maritime Anomaly Detection, Ph.D. Thesis, University of Örebro, Sweden. http://oru.diva-portal.org/smash/get/diva2:381336/FULLTEXT06.pdf.Google Scholar
Stacy, E.W. (1962). A generalization of the gamma distribution. The Annals of Mathematical Statistics, September 33, 1187–1192.Google Scholar
Su, Y.Y. and Chang, S.J. (2008). Spatial Cluster Detection for the Fishing Vessel Monitoring Systems. MTS/IEEE Kobe Techno-Ocean OCEANS 2008, 8–11 April, doi: 10.1109/OCEANSKOBE.2008.4531048 Google Scholar
Willems, N., Wetering, H.V.D. and Wijk, J.J.V. (2009). Visualization of vessel movements. Computer Graphics. Forum 2009, 28, 959–966.Google Scholar
Zhang, B., Dong, H. and Liu, T. (2013). Research on Marine Solid State Radar and its Application. 2013 Third World Congress on Information and Communication Technologies (WICT). IEEE, doi: 10.1109/WICT.2013.7113098.Google Scholar