DocumentCode :
59823
Title :
Knowledge-Based Multitarget Ship Tracking for HF Surface Wave Radar Systems
Author :
Vivone, Gemine ; Braca, Paolo ; Horstmann, Jochen
Author_Institution :
Centre for Maritime Res. & Experimentation, North Atlantic Treaty Organ., Rome, Italy
Volume :
53
Issue :
7
fYear :
2015
fDate :
Jul-15
Firstpage :
3931
Lastpage :
3949
Abstract :
These last decades spawned a great interest toward low-power high-frequency (HF) surface-wave (SW) radars for ocean remote sensing. By virtue of their over-the-horizon coverage capability and continuous-time mode of operation, these sensors are also effective long-range early warning tools in maritime situational awareness applications providing an additional source of information for target detection and tracking. Unfortunately, they also exhibit many shortcomings that need to be taken into account, and proper algorithms need to be exploited to overcome their limitations. In this paper, we develop a knowledge-based (KB) multitarget tracking methodology that takes advantage of a priori information on the ship traffic. This a priori information is given by the ship sea lanes and by their related motion models, which together constitute the basic building blocks of a variable structure interactive multiple model procedure. False alarms and missed detections are dealt with using a joint probabilistic data association rule and nonlinearities are handled by means of the unscented Kalman filter. The KB-tracking procedure is validated using real data acquired during an HF-radar experiment in the Ligurian Sea (Mediterranean Sea). Two HFSW radar systems were operated to develop and test target detection and tracking algorithms. The overall performance is defined in terms of time-on-target, false-alarm rate (FAR), track fragmentation (TF), and accuracy. A full statistical characterization is provided using one month of data. A significant improvement of the KB-tracking procedure, in terms of system performance, is demonstrated in comparison with a standard joint probabilistic data association tracker recently proposed in the literature to track HFSW radar data. The main improvement of our approach is the better capability of following targets without increasing the FAR. This increment is much more evident in the region of low FAR, where it can be over the 30% for both the HF- W radar systems. The KB-tracking exhibits on average a reduction of the TF of about the 20% and the 13% of the utilized HFSW-radar systems.
Keywords :
Kalman filters; data mining; geophysics computing; nonlinear filters; oceanographic techniques; radar detection; radar tracking; remote sensing by radar; ships; target tracking; FAR; HF surface wave radar system; HFSW radar systems; KB-tracking procedure; continuous time mode; early warning tools; false alarm detection; false alarm rate; joint probabilistic data association rule; knowledge-based multitarget ship tracking; maritime situational awareness applications; missed detection; motion model; ocean remote sensing; over-the-horizon coverage capability; ship sea lanes; ship traffic; standard joint probabilistic data association tracker; target detection; target tracking; time-on-target; track fragmentation; unscented Kalman filter; variable structure interactive multiple model procedure; Clutter; Marine vehicles; Noise; Radar tracking; Sea surface; Target tracking; High-frequency surface-wave (HWSW) radar; knowledge-based (KB) tracking; maritime surveillance; target detection; target tracking;
fLanguage :
English
Journal_Title :
Geoscience and Remote Sensing, IEEE Transactions on
Publisher :
ieee
ISSN :
0196-2892
Type :
jour
DOI :
10.1109/TGRS.2014.2388355
Filename :
7036116
Link To Document :
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