Data fusion performance of HFSWR Systems for ship traffic monitoring

In Maritime Situational Awareness (MSA), low-power High-Frequency Surface-Wave (HFSW) radars fit the role of long-range early-warning tools by virtue of their over-the-horizon (OTH) coverage. Unfortunately these sensors, developed mainly for ocean remote sensing applications, exhibit poor range and azimuth resolution, high non-linearity and significant false alarm rate due to clutter and interference. For these reasons, the Joint Probabilistic Data Association (JPDA) logic, followed by the Unscented Kalman Filter (UKF), is proposed. Then, to exploit two simultaneously operating HFSW radars with overlapping fields of view, a track-to-track association and fusion (T2T-A/F) logic is applied. The capabilities of the JPDA-UKF tracking algorithm in combination with the T2T-A/F strategy are evaluated using a set of purpose-defined performance metrics, such as the time-on-target (ToT), the false alarm rate (FAR) and the root mean square error (RMSE). Special attention is paid to the comparison of the JPDA-UKF with the 3D (rangeazimuth-doppler) Ordered Statistics Constant False Alarm Rate (OS-CFAR) detection algorithm. A procedure based on track length modelling for the analysis of true and false tracks is presented as well. Single-sensor and multi-sensor tracking performances are investigated using real data collected during the NATO Battlespace Preparation 2009 (BP09) HF-radar experiment, which took place between May and December 2009 in the Mediterranean Sea. Ship reports from the Automatic Identification System (AIS) are used as ground truth information. Experimental results are reported and discussed.