A new method to estimate ship velocity vector for spaceborne SAR
A new method to estimate ship velocity vector for spaceborne SAR
- Author(s): Y. Huang 1 ; D. Li 1 ; Z. Dong 1
- DOI: 10.1049/icp.2021.0819
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- Author(s): Y. Huang 1 ; D. Li 1 ; Z. Dong 1
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View affiliations
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Affiliations:
1:
Colledge of Electronic Science and Technology , National University of Defense Technology , Changsha, Hunan , P. R. China
Source:
IET International Radar Conference (IET IRC 2020),
2021
p.
591 – 596
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Affiliations:
1:
Colledge of Electronic Science and Technology , National University of Defense Technology , Changsha, Hunan , P. R. China
- Conference: IET International Radar Conference (IET IRC 2020)
- DOI: 10.1049/icp.2021.0819
- ISBN: 978-1-83953-540-6
- Location: Online Conference
- Conference date: 04-06 November 2020
- Format: PDF
Ship velocity estimation algorithms for spaceborne synthetic aperture radar (SAR) play an important role in monitoring major waters and ports. A new method to estimate the velocity of moving ships in spaceborne SAR is proposed, which can get the ground range speed and azimuth speed of ship,as well as improve refocusing quality. This method is applied directly on the single-look complex (SLC) image data, and a Map drift (MD) algorithm and extraction geometric features of ships are introduced to derive its two-dimensional velocity formula. Furthermore, it does not require external data support. Finally, real strip-map SAR image from GF-3 satellite was used to test the proposed method. The estimated velocities are exactly matched with the data recorded by the international automatic identification system (AIS). Moreover, compared with present method, the proposed method can get vessels’ full speed information with higher accuracy, and greatly improve refocusing quality at the same time.
Inspec keywords: spaceborne radar; ships; remote sensing by radar; synthetic aperture radar; radar imaging
Subjects: Velocity, acceleration and rotation measurement; Radar equipment, systems and applications; Probability theory, stochastic processes, and statistics; Optical, image and video signal processing; Computer vision and image processing techniques; Instrumentation and techniques for geophysical, hydrospheric and lower atmosphere research