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Analysis and Machine Intelligence, Vol. 35, 2013, No 4, 898-910. 4. Andriyenko, A., K. Schindler. Multi-Target Tracking by Continuous Energy Minimization. - In: IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, 2011, 1265-1272. 5. Comaniciu, D., V. Ramesh, P. Meer. The Variable Bandwidth Mean Shift and Data- Driven Scale Selection. - In: IEEE International Conference on Computer Vision,Vancouver, BC, 2001, 438-445. 6. Tomasi, C., T. Kanade. Detection and Tracking of Point Features. Technical Report CMUCS-91-132, Carnegie Mellon University

Abstract

This paper presents the main technical characteristics and working performances of coastal maritime surveillance radars, such as low-power High-Frequency Surface Wave Radars (HFSWR) and Over the Horizon Radars (OTHR). These radars have demonstrated to be a cost-effective long-range early-warning sensor for ship detection and tracking in coastal waters, sea channels and passages. In this work, multi-target tracking and data fusion techniques are applied to live-recorded data from a network of oceanographic HFSWR stations installed in Jindalee Operational Radar Network (JORN), Wellen Radar (WERA) in Ligurian Sea (Mediterranean Sea), CODAR Ocean Sebsorsin and in the German Bight (North Sea). The coastal Imaging Sciences Research (ISR) HFSWR system, Multi-static ISR HF Radar, Ship Classification using Multi-Frequency HF Radar, Coastal HF radar surveillance of pirate boats and Different projects of coastal HF radars for vessels detecting are described.

Ship reports from the Automatic Identification System (AIS), recorded from both coastal and satellite Land Earth Stations (LES) are exploited as ground truth information and a methodology is applied to classify the fused tracks and to estimate system performances. Experimental results for all above solutions are presented and discussed, together with an outline for future integration and infrastructures.

References [1] B. Benfold, I. Reid, Stable multi-target tracking in real-time surveillance video, IEEE Conference on Computer Vision and Pattern Recognition, pp.3457-3464, 20-25 June 2011 [2] K. Bernardin, R. Stiefelhagen, Evaluating multiple object tracking performance: the CLEARMOT metrics, Journal Image Video Processing, , Hindawi Publishing Corp., New York, NY, United States, pp. 1-10, February 2008 [3] G. Bradski, V. Pisarevsky, Intel’s computer vision library: applications in calibration, stereo segmentation, tracking, gesture, face and object recognition

Conference on Information and Automation (ICIA’2010), 2010, 470-475. 13. Shu, Yuan, Yuan Donghui, Sun Jizhou, Liu Yongbo, Li Jing, Yuan Lin. The Application of AC-GA on Multi-Sensor Multi-Target Tracking. - Chinese Journal of Electronics, Vol. 41, 2013, No 3, 609-614. 14. Maksarov, D, H. Durrant-Whyte. Mobile Vehicle Navigation in Unknown Environments: A Multiple Hypothesis Approach. - IEEE Proc. Control Theory Application, Vol. 142, 1995, No 4, 385-391. 15. Guivant, J., E. Nebot. Optimization of the Simultaneous Localization and Map-Building Algorithm for Real

under corrupting noise by use of multirate Kalman filter, Circuits, Systems, Signal Processing 14 (6): 771-786. Del Favero, S. and Zampieri, S. (2009). Distributed estimation through randomized gossip Kalman filter, Proceedings of the 48th IEEE Conference on Decision and Control . Fitzgerald, R. (1985). Track biases and coalescence with probabilistic data association, IEEE Transactions on Aerospace and Electronic Systems 21 (6): 822-825. Fortmann, T., Bar-Shalom, Y. and Scheffe, M. (1980). Multi-target tracking using joint probabilistic data association

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