In order to efficiently cover maritime areas at over the horizon (OTH) distances and thus increase marine safety in a nation’s exclusive economic zone (EEZ), a network of maritime sensors built around High Frequency Surface Wave Radars (HFSWR) can be an excellent choice. The critical parameter for success of the deployed sensor network is a real time tracking of all detected vessels. During the tracking process, data association (DA) is the first step and it defines the complexity and thus the speed of the whole tracking process. This paper presents a density based clustering DA procedure where the cluster complexity determines the applied DA procedure within the cluster itself. It is demonstrated that the great majority of clusters (over 98 % of all clusters in the worst case) may be processed in a timely manner with an optimal DA procedure, or more precisely, a Joint Probability Data Association (JPDA). However, a small number of unusually large clusters (less than 2 % of all clusters) requires the application of a sub’ optimal DA procedure, more accurately, the Roecker’s suboptimal JPDA algorithm, in order to maintain real time performance of the whole tracking process. Moreover, unlike standard JPDA procedure which tends to be inapplicable for real time tracking in heavily cluttered environment, the density based clustering DA procedure presented here provides real time performances in the very same environment. The whole analysis is done on real HFSWR data obtained from two HFSWR, located in the Gulf of Guinea. The data set used for the experiments includes data obtained during a month and a half of constant HFSWR operation.

This paper demonstrates the benefits that high-frequency surface wave radars (HFSWR) are bringing to maritime safety and security in off-shore activities at over the horizon distances. As a primary means for remote sensing of marine and maritime environment, a network of HFSWRs is deployed in the western part of the Gulf of Guinea and covers an area of over 100 km². Alongside HFSWRs, usual maritime sensors are utilized for vessel tracking as well, however, only satellite automatic identification systems (SAIS) and land automatic identification systems (LAIS) are capable of covering over the horizon distances. Unfortunately, both LAIS and SAIS require vessel cooperation in order to provide any data, which is often abused by vessels conducting illegal activities. Here, analysis is done in which AIS and HFSWR data are compared in order to identify a pattern of behavior of non–cooperative vessels (vessels with onboard AIS devices turned off) so a proper risk assessment may be achieved. It is shown that typical patterns can be easily recognized for two illegal activities which plague the waters where this study is conducted. Those illegal activities are oil bunkering and piracy, both conducted off-shore and out of the reach of the usual coastal sensors such as X or S band radars. Furthermore, tracks created whilst conducting illegal activities are easily distinguishable from others in the overall operational picture. Additionally, it should be pointed out that numerous vessels are switching off their AIS devices when they leave the coastal regions in order to avoid detection by pirate vessels. This behavior can also be easily recognized and must not be mixed with the illegal activities mentioned above.