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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)
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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 

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References

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