A Maritime Radar Network for Surface Traffic Control Based on Service Vessels Francesco Sermi*, Clio Mugnai*, Fabrizio Cuccoli*, Luca Facheris ** *
National Laboratory of Radar and Surveillance Systems of CNIT, c/o Dipartimento Ingegneria dell’Informazione, Università di Firenze Via di Santa Marta, 3, 50139 Firenze, Italy.
[email protected] [email protected] [email protected] **
Dipartimento Ingegneria dell’Informazione, Università di Firenze Via di Santa Marta, 3, 50139 Firenze, Italy
[email protected]
Abstract: In this paper we present the outline of a method to monitor maritime traffic in dense traffic areas toward the fusion of radar data from a fleet of co-operating vessels operating in the considered scenario. The method is based on the employment of the navigational radar and the AIS transmitter, generally present on board of service vessels (such as ferries or cargos), and of an inland operative station able to synchronize and fuse the data received from the homogeneous moving sensors.
1. Introduction The monitoring and controlling of maritime surface traffic in particularly congested areas is, today more than ever, a priority task for what concerns safety and security at sea [1]. While several tools, as Vessel Traffic Services (VTS) [2], optical sensors and networks of cameras, help to accomplish this issue in harbors [3] and relatively narrow sea areas, several problems arise in regions located away from the coast, out of the range of conventional microwave coastal radars. Since in most cases, commercial traffic lives together with service traffic, and several other typologies of surface traffic, surveillance of fixed radar control stations can be in principle replaced or integrated with that made available by the radars of mobile co-operating vessels. Based on a regular flux of co-operating service vessels, a ground station can control traffic, if it regularly receives plots formed by on board radars. Nowadays every type of vessel, from the shipping boat, to the cargo ship, is equipped with at least one radar system, typically H-polarized S or X band, that provides meteorological info and data relative to eventual detections (due to coastline, other vessels or obstacles as rocks, icebergs, etc). All these information are usually employed only by the ship captain, together with data provided by forecast offices, coastguard, and other bureaus, to establish route diversions, mainly for three different purposes: safety (e.g. obstacles avoidance, storm circumnavigation, etc), security (avoidance of piracy or war areas) and efficiency (e.g. fuel saving by route shortening) of the navigation. The proposed surveillance system relays on the idea that the data collected by the single onboard radar systems could be shared and collected by an inland operative unite that synchronizes the data stream, processes the received information and provides a global representation of surface traffic in the interested area to be shared among the co-operating vessels and the other users that require it (see the sketch in Fig.1). In order to deliver the ship-borne radar detections to the operative station, we propose the employment of the Automatic Identification System (AIS) [4]: a device already presents on
all passengers vessels and on ships exceeding a certain tonnage and charged for the broadcast of data relative to the vessel ID and activity (identification code, shipment, location and dates of departure and presumed arrival, etc). Then the protocol to be employed for data transmission from the co-operative vessels to the operative station could be a pseudo-AIS code, that is the conventional AIS data stream enriched with parameters relative to eventual detections (coordinates, speed, bearing, RCS, etc). Note that a possible limit to the implementation of AIS devices as transmitters for radar data too could be the availability of the necessary bandwidth.
Figure 1. Sketch of the proposed radar system in a scenario with 4 co-operating vessels (c#), one coastal radar (coast#) and 7 unknown targets (u#).
2.
System Model and Simulated Scenario
A preliminary investigation on the benefits, in terms of space/time radar coverage, given from a system of co-operating vessels sharing their radar measurements is given in [5]. Hence the same system, referred to as “Radar Network of Co-operative Vessels” (RNCV) was proposed and further investigated by the CNIT within the EC 7th framework project: “SeaBILLA: Sea Border Surveillance”. This paper is aimed to provide a general description of the RNCV method and its state of the art and to fix several basic hypothesis for the modeling of the RNCV system and the maritime scenario necessary to test its performances [6]. A) System Model Figure 2 shows the block diagram and the information flow among the various systems involved. The block “Scenario Management System” represents a shell that encloses the RNCV system. It is fed with radar data from co-operative vessels (processed directly by the RNCV) and from other sensors (VTS, camera networks, etc…) and with returns from the Identification and Classification (I&C) block triggered by identification request for potential threats. Once a threat is confirmed, the Scenario Management System sends an action request to the competent authority (coast guard, military unit in the area, etc.) together with all the data relative to the threat (collected values of RCS, position, heading and speed).
The central block performs the data fusion [7] (collecting radar data, synchronizing the data flow, etc.), some pre-processing of the collected data (range-gating procedure, association observations-to-targets, etc.) and all the other functions needed to manage a maritime traffic scenario (tracking of the detected vessels and pre-classification of unknown vessels in “potential threats”).
Figure 2. Block diagram of the system aim to the management of a maritime surface traffic scenario with information from a radar network of co-operative vessels and from various heterogeneous sensors.
The pre-classification is based on cinematic and positioning roles that are generally scenariorelated and that must be defined within the RNCV system (i.e. max speed and acceleration, proximity to commercial routes, entrance in sensitive areas, etc.) B) Simulated Scenario The geographic scenario selected for the simulation of the system functionalities is one of the study cases described in the previously mentioned project SeaBILLA [6]. We consider the Sicily channel, in particular a geographical box with the following boundaries: [34.5 - 37.5]° in latitude [10 - 15.5]° in longitude. In such region (Fig. 3), we simulate the presence of service vessels (a fleet of ferries) operating their daily schedule (yellow lines) and of several unknown vessels of different types (red lines): cargo ships covering the Suez-Gibraltar route; some fishing vessels; a slow wooden boat living the Libyan coast (near Zuara) to Malta island; some small fast boats. In order to generate radar coverage maps, the selected area is divided into resolution cells of four squared kilometers. Note that this is not the resolution of the ship-borne radar, but just the resolution of the produced coverage maps. Coastline profiles for computational purposes are extracted from NOAA medium resolution shoreline data. The whole processing is carried out with Matlab 7.1, while simulation results are presented via the Matlab embedded graphic tool and also converted to kml format to be displayed in Google Earth. The routes of unknown and co-operating vessels can be drown via the Google Earth’s graphic interface, than they are saved as kml files, acquired by Matlab, converted into shp files and finally interpolated according with vessel timetables, speed and simulation time-step. All parameters relative to co-operating ships (speed, radar range, departure/destination harbors, timetables, etc) and unknown vessels (type, activity, etc) are passed to the simulator via two text files with pre-established layout. The co-operating vessels are supposed to be ferries that are carrying out their daily scheduled routine. They all mount onboard a navigational radar with an average maximum range of 13
nm (approximately 24 km) and they move uniformly at a constant speed that ranges from 20 to 40 knots. Differently, the fleet of unknown vessels is represented by an heterogeneous group of boats (fishing boats, cargo ships, slow wooden boats, off-shore fast boats, etc) with changeable speed and different cinematic features. Despite every unknown vessel is detected and tracked as much as possible by the system, only the ones that it labels as “potential threats” need to be identified.
Fig. 3. Google Earth snapshot (altitude ≅1250 km a.m.s.l.) of the considered scenario (Sicily channel) with routes of co-operating (yellow) and unknown (red) vessels. The white lines represent the simulated coastal radar coverage zone
3.
Simulation Setup and Scenario Results
We set some reasonable timetables both for the ferries and for the unknown vessels (considering the month of August, when the traffic in the area is particularly dense) and we run the simulation of their 24 hours activity, recording their geographic position with a time step of one minute.
Fig. 5. Simulated dynamic scenario in the Sicily channel: red points (blue crosses) represents the position of unknown targets (ferries) every 30 minute within the 24 hours. Yellow and Green lines shows the activity of unknown vessels and ferries during the selected 6 hours: 9am to 3pm
Figure 5 shows the activity of co-operating and unknown characters in the area of interest, assuming a 6 hours interval from 9am to 3pm.
Fig. 6. Radar coverage maps generated with Matlab and relative to the dynamic scenario presented in Fig. 5.
The developed software tools can now perform a space/time radar coverage analysis in the given scenario as represented in figure 6 that shows, on the geographic map, the percentage of time-radar coverage during the selected 6 hours. Note that the graphic does not account for the skip-zone that surrounds the vessels for about 20 meters. Since the employed sensors are moving, only in proximity of the Valletta’s port on the island of Lampedusa a 100% time coverage is provided. In correspondence of the ferries routes an averaged 45% of radar coverage is available in the considered time interval.
Conclusions After the analysis of different scenarios of maritime traffic, it appears evident that the only requirement for an effective employment of the RNCV system is an adequate volume of traffic of co-operating vessels compared to the surface of the considered geographic scenario. In fact, once interfaced with an Identification and Classification System and employed together with fixed coastal radar sensors, the RNCV can represent a quite interesting tool for the monitoring of complex maritime scenarios in dense traffic areas. Its greatest strength is represented by the ability to refer to well known technologies and to employ two devices (the ship-borne radar and the AIS transmitter) that are already present on board of most ships, but employed with different purposes. Such approach would considerably knocks down the economical cost for the physical implementation of the proposed system. Besides the outline of the method, we provided here a basic description of the RNCV system, we introduced the main hypothesis made for the definition of the scenario where to test the method and we showed an example of the space/time coverage maps that demonstrate the benefits brought by the system to any coastal surveillance system in terms of radar coverage. The developed software represents the first block of the test bed for the proposed method. We are currently developing models for the sea clutter [8],[9] and the fluctuating targets in order
to enrich the space/time radar coverage maps with maps of probability of detection [10]. Further investigations must be conducted also on the internal timing of the system and on the employed data fusion techniques [7], particularly for what concerns the synchronization and the association observation-to-target.
Acknowledgments This work has been funded by the European Community within the FP VII project SeaBILLA (Sea Border Surveillance).
References: [1] [2]
International Convention for the Safety of Life at Sea (SOLAS) - Regulation XI/3 (1994). B. Mohin, “Marine traffic management, a new concept in VTS”, Proceedings of the Ports ‘95 Conference on Port Engineering and Development for the 21th century - Tampa 13-15 March 1995, pp. 449-453. [3] IMO/ILO - MESSHP-2003-14 – “Code of Practice on Security in Ports”. [4] Recommendation ITU-R M.1371-4 (04/2010), “Technical characteristics for an automatic identification system using time-division multiple access in the VHF maritime mobile band”. [5] D. Giuli, L. Facheris, M. Gherardelli, G. Cambi, “Simulation of an integrated system for matitime traffic control in a co-operative environment” Proceedings 5th International Conference on Radar Systems, 17-21 may 1999, Brest (France). [6] http://www.seabilla.eu/cms/seabilla [7] E. Waltz, J. Llinas, “Multisensor Data Fusion” - Artech House, Norwood, MA, 1990. [8] E. Conte, M. Longo, M. Lops, “Modelling and simulation of non-Rayleigh radar clutter”, IEE proceedings-f, vol. 138, No. 2, April 1991. [9] K.D. Ward, R.J.A. Tough, S. Watts, “Sea Clutter : Scattering, the K Distribution and Radar Performance”, Institution of Engineering and Technology, 2006. [10] S. Watts, “Radar detection prediction in sea clutter using the compound K-distribution model”, IEE Proceedings, Vol.132,Pt.F, No.7, December 1985. [11] D.A. Shnidman, “Radar Detection in Clutter”, IEEE Transactions On Aerospace And Electronic Systems, Vol. 41, No. 3 July 2005.
