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Cited by 88Target Tracking and Data Fusion in Sensor NetworksRadar Systems and Signal ProcessingOcean Waves and Remote Sensing

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Knowledge-Based Multitarget Ship Tracking for HF Surface Wave Radar Systems

Vivone, Gemine, Braca, Paolo, Horstmann, Jochen·2015·IEEE Transactions on Geoscience and Remote Sensing
DOI 10.1109/tgrs.2014.2388355
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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 HFSW 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.

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Metadata

Authors
Vivone, G., Braca, P., & Horstmann, J.
Affiliations
  • NATO Centre for Maritime Research and Experimentation (IT)
  • Helmholtz-Zentrum Hereon (DE)
ORCID
Venue
IEEE Transactions on Geoscience and Remote Sensing
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2015-07
Volume
53
Issue
7
Pages
3931-3949
ISSN
  • 0196-2892 (ISSN-L)
  • 1558-0644
DOI
10.1109/tgrs.2014.2388355
OpenAlex
W2018065034
Cited by
88
References
1
Citations by year
2026: 2 · 2025: 5 · 2024: 7 · 2023: 10 · 2022: 4 · 2021: 13 · 2020: 7 · 2019: 12 · 2018: 6 · 2017: 7 · 2016: 11 · 2015: 4
FWCI
9.16
Type
journal article
Language
en
Open access
closed
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Locations
  • IEEE Transactions on Geoscience and Remote Sensing (publishedVersion)
  • Helmholtz-Zentrum Geesthacht Zentrum für Materialforschung und kustenforschung (The Helmholtz Center Geesthacht) (submittedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Target Tracking and Data Fusion in Sensor Networks
  • Physical Sciences › Engineering › Aerospace Engineering › Radar Systems and Signal Processing
  • Physical Sciences › Earth and Planetary Sciences › Oceanography › Ocean Waves and Remote Sensing
Keywords
multi-target tracking, high frequency surface wave radar, vessel tracking, maritime situational awareness, joint probabilistic data association, unscented Kalman filter, false alarm rate, track fragmentation, target detection, over-the-horizon radar, Ligurian Sea

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