System for surveillance of maritime traffic using the network of over-the-horizon radars

Ova disertacija se bavi optimizacijom i prilagođenjem algoritama za praćenje ciljeva za potrebe ublažavanja uticaja štetnih refleksija na proces praćenja ciljeva kod izahorizontskih radara sa površinskim talasom (HFSW radari), upotrebljenih za osmatranje pomorskih ciljeva i konceptualizacijom i realizacijom sistema za integrisano pomorsko praćenje ciljeva baziranog na HFSWR mrežama.This dissertation deals with the optimization and adaptation of target tracking algorithms to mitigate the impact of harmful reflections on the target tracking process in over-the-horizon high frequency surface wave radar (HFSW radar), used to observe maritime targets and conceptualize and implement an integrated maritime surveillance system based on HFSWR networks..

Practical Moving Target Detection in Maritime Environments Using Fuzzy Multi-sensor Data Fusion

As autonomous ships become the future trend for maritime transportation, it is of importance to develop intelligent autonomous navigation systems to ensure the navigation safety of ships. Among the three core components (sensing, planning and control modules) of the system, an accurate detection of target ships’ navigation information is critical. Within a typical maritime environment, the existence of sensor noises as well as the influences generated by varying environment conditions largely limit the reliability of using a single sensor for environment awareness. It is therefore vital to use multiple sensors together with a multi-sensor data fusion technology to improve the detection performance. In this paper, a fuzzy logic-based multi-sensor data fusion algorithm for moving target ships detection has been proposed and designed using both AIS and radar information. A two-stage fuzzy logic association method has been particularly developed and integrated with Kalman filtering to achieve a computationally efficient performance. The effectiveness of the proposed algorithm has been tested and validated in simulations where multiple target ships are transiting with complex movements

Mathematical Models and Monte-Carlo Algorithms for Improved Detection of Targets in the Commercial Maritime Domain

Commercial Vessel Traffic Monitoring Services (VTMSs) are widely used by port authorities and the military to improve the safety and efficiency of navigation, as well as to ensure the security of ports and marine life as a whole. Technology based on the Kalman Filtering framework is in widespread use in modern operational VTMS systems. At a research level, there has also been a significant interest in Particle Filters, which are widely researched but far less widely applied to deliver an operational advantage. The Monte-Carlo nature of Particle Filters places them as the ideal candidate for solving the highly non-linear, non-Gaussian problems encountered by modern VTMS systems. However, somewhat counter-intuitively, while Particle Filters are best suited to exploit such non-linear, non-Gaussian problems, they are most frequently used within a context that is mostly linear and Gaussian. The engineering challenge tackled by the PhD project reported in this thesis was to study and experiment with models that are well placed to capitalise on the abilities of Particle Filters and to develop solutions that make use of such models to deliver a direct operational advantage in real applications within the commercial maritime domain

Application of the JPDA-UKF to HFSW radars for maritime situational awareness

At the present day, growing interest is paid to the development of more reliable surveillance systems for maritime situational awareness (MSA). The purpose is to detect, track and classify cooperative and non-cooperative targets. For this reason, great interest is given to low-power/cost High-Frequency Surface-Wave (HFSW) radars as an early-warning tool for over-the-horizon (OTH) applications. However, in HFSW radars there is a trade-off in terms of quality and cost, i.e. the radar system exhibits poor azimuth resolution, high non-linearity, and significant false alarm rate. All these aspects reduce tracking performance if not properly addressed. In this context, the Joint Probabilistic Data Association (JPDA) with the Unscented Kalman Filter (UKF) is proposed. The tracking algorithm behavior is investigated by a comparison between the tracks generated by two HFSW radars, with overlapped fields of view, and Automatic Identification System (AIS) data. A discussion is provided about the possible effectiveness of HFSW radar fusion strategies. Preliminary results from a HFSW Radar experiment are reported and discussed