Automatic radar plotting aid

Abstract: Marine radars mounted on ships can provide remarkable insights into ocean behaviour from distances of several kilometres, placing other in situ observations and the environment around a ship into a wider oceanographic context. It has been known for some time that it is possible to map shallow water bathymetry and currents using radar image sequences recorded from shore based stations. However, a long standing question from military and hydrographic communities has been whether such techniques can be applied to radar data collected by moving vessels. If so, this presents the possibility of mapping large areas of shallow or coastal seas (albeit with a somewhat coarse horizontal resolution of 50–100 m) prior to the surveying vessel actually having to travel into potentially uncharted or dangerous shallow water areas. Trial sets of radar data were recorded by the Canadian Forces Auxiliary Vessel Quest using a Wamos radar digitiser connected to a Decca navigation radar during a number of deployments around Nova Scotia in 2008 and 2009. Georeferencing corrections derived from the existing ship navigation systems were sufficient to allow the application of the existing depth inversion analysis designed for static radar installations. This paper presents the results of bathymetry analyses of two datasets recorded from CFAV Quest while the vessel was travelling at speeds of up to 14 knots. The bathymetry derived from the radar data compare favourably with independent surveys and with the on-board echo sounder to depths of approximately 50 m.

Abstract: Basic radar principles The radar system - operational principles Target detection Automatic radar plotting aids (ARPA) ARPA - additional facilities The radar system - operational controls Radar plotting Navigation techniques using radar and ARPA ARPA - accuracy and errors Integrated Bridge Systems IMO requirements for Masters and Yacht radar usage.

Abstract: Recently most maritime countries in the world have fitted automatic identification system (AIS) in vessel traffic services (VTS) in compliance with regulations of International Convention in order to identify ship name and collect ship information. Traditionally VTS operators use automatic radar plotting aids (ARPA) to get ship maneuvering information for preventing ships from navigational faults. Although the two equipments can provide similar functions for VTS, the operational theory of them is quite different. In this study an experimental observation was carried out by means of ARPA radar and AIS fitted in the building of Merchant Marine Department, NTOU. The detected information was analyzed for comparison of their difference on characteristics. Results reveal that AIS can detect target ships with wider area coverage, larger quantity and more voyage information than ARPA radar. The latter can provide an active monitor to detect small ships that can not be done by AIS. Consequently for navigation safety VTS operators must concentrate on ARPA radar in priority and use AIS as supplementary installation for identification of ships.

Abstract: Maritime ARPA, Automatic Radar Plotting Aid, systems often complicate navigation by mistaking channel structures and land objects for vessels in inland rivers and harbors. By using Fuzzy C-Means (FCM), it is possible to construct an artificial intelligence to classify and identify ARPA target types and calculate the possibility of a target being a real vessel based on the target’s speed over ground, vector over ground, and location. The membership functions of each attribute are constructed using statics, expert knowledge, and electronic chart information. The main difficulty in developing a successful FCM framework to achieve the previously stated goals is the determination of a proper method of calculating the classification number C and fuzzy coefficient m. Because the value of C for the case of ARPA targets classification is finite, the best C would be determined by assessing the Euclidean distance. The value of m is related to the discreteness of the evidence and results, which is evaluated using the Shannon entropy and the gain. A number of methods exist to properly evaluate the contributions from different forms of evidence so that the best m can be found using the tendentiousness of the evidence. In field testing, the improved FCM was able to accurately classify the ARPA targets, decrease the workload on the ship’s officer, and increase safety.