Range-Doppler Based CFAR Ship Detection with Automatic Training Data Selection Article Swipe
Sushil Kumar Joshi
,
Stefan V. Baumgartner
,
André Barros Cardoso da Silva
,
Gerhard Krieger
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.3390/rs11111270
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.3390/rs11111270
Ship detection is an essential maritime security requirement. Current state-of-the-art synthetic aperture radar (SAR) based ship detection methods employ fully focused images. The time-consuming processing efforts required to generate these images make them generally unsuitable for real time applications. This paper proposes a novel real time oriented ship detection strategy applicable to range-compressed (RC) radar data acquired by an airborne radar sensor during linear, circular and arbitrary flight tracks. A constant false alarm rate (CFAR) detection threshold is computed in the range-Doppler domain using suitable distribution functions. Detection in range-Doppler has the advantage that principally even small ships with a low radar cross section (RCS) can be detected if they are moving fast enough so that the ship signals are shifted to the exo-clutter region. In order to determine a robust threshold, the ocean statistics have to be described accurately. Bright target peaks in the background ocean data bias the statistics and lead to an erroneous threshold. Therefore, an automatic ocean training data extraction procedure is proposed in the paper. It includes (1) a novel target pre-detection module that removes the bright peaks from the data already in time domain, (2) clutter normalization in the Doppler domain using the remaining samples, (3) ocean statistics estimation and (4) threshold computation. Various sea clutter models are investigated and analyzed in the paper for finding the most suitable models for the RC data. The robustness and applicability of the proposed method is validated using real linearly and circularly acquired radar data from DLR’s (Deutsches Zentrum für Luft- und Raumfahrt) airborne F-SAR system.
Related Topics
Concepts
Computer science
Clutter
Constant false alarm rate
Remote sensing
Synthetic aperture radar
Radar
False alarm
Doppler effect
Artificial intelligence
Computer vision
Moving target indication
Radar imaging
Continuous-wave radar
Geology
Telecommunications
Physics
Astronomy
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/rs11111270
- OA Status
- gold
- Cited By
- 55
- References
- 48
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2947256382
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2947256382Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/rs11111270Digital Object Identifier
- Title
-
Range-Doppler Based CFAR Ship Detection with Automatic Training Data SelectionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-05-28Full publication date if available
- Authors
-
Sushil Kumar Joshi, Stefan V. Baumgartner, André Barros Cardoso da Silva, Gerhard KriegerList of authors in order
- Landing page
-
https://doi.org/10.3390/rs11111270Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.3390/rs11111270Direct OA link when available
- Concepts
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Computer science, Clutter, Constant false alarm rate, Remote sensing, Synthetic aperture radar, Radar, False alarm, Doppler effect, Artificial intelligence, Computer vision, Moving target indication, Radar imaging, Continuous-wave radar, Geology, Telecommunications, Physics, AstronomyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
55Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 9, 2024: 17, 2023: 6, 2022: 10, 2021: 4Per-year citation counts (last 5 years)
- References (count)
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48Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| display_name | Range-Doppler Based CFAR Ship Detection with Automatic Training Data Selection |
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