ISAR Imaging for Maneuvering Targets with Complex Motion Based on Generalized Radon-Fourier Transform and Gradient-Based Descent under Low SNR Article Swipe
Zhijun Yang
,
Dong Li
,
Xiaoheng Tan
,
Hongqing Liu
,
Yuchuan Liu
,
Guisheng Liao
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/rs13112198
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/rs13112198
The existing inverse synthetic aperture radar (ISAR) imaging algorithms for ship targets with complex three-dimensional (3D) rotational motion are not applicable because of continuous change of image projection plane (IPP), especially under low signal-to-noise-ratio (SNR) condition. To overcome this obstacle, an efficient approach based on generalized Radon Fourier transform (GRFT) and gradient-based descent optimal is proposed in this paper. First, the geometry and signal model for nonstationary IPP of ship targets with complex 3-D rotational motion is established. Furthermore, the two-dimensional (2D) spatial-variant phase errors caused by complex 3-D rotational motion which can seriously blur the imaging performance are derived. Second, to improve the computational efficiency for 2-D spatial-variant phase errors compensation, the coarse motion parameters of ship targets are estimated via the GRFT method. In addition, using the gradient-based descent optimal method, the global optimum solution is iteratively estimated. Meanwhile, to solve the local extremum for cost surface obtained via conventional image entropy, the image entropy combined with subarray averaging is applied to accelerate the global optimal convergence. The main contributions of the proposed method are: (1) the geometry and signal model for ship targets with a complex 3-D rotational motion under nonstationary IPP are established; (2) the image entropy conjunct with subarray averaging operation is proposed to accelerate the global optimal convergence; (3) the proposed method can ensure the imaging accuracy even with high imaging efficiency thanks to the sole optimal solution generated by using the subarray averaging and image entropy. Several experiments using simulated and electromagnetic data are performed to validate the effectiveness of the proposed approach.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/rs13112198
- https://www.mdpi.com/2072-4292/13/11/2198/pdf?version=1622809631
- OA Status
- gold
- Cited By
- 13
- References
- 34
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3172074138
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3172074138Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/rs13112198Digital Object Identifier
- Title
-
ISAR Imaging for Maneuvering Targets with Complex Motion Based on Generalized Radon-Fourier Transform and Gradient-Based Descent under Low SNRWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-06-04Full publication date if available
- Authors
-
Zhijun Yang, Dong Li, Xiaoheng Tan, Hongqing Liu, Yuchuan Liu, Guisheng LiaoList of authors in order
- Landing page
-
https://doi.org/10.3390/rs13112198Publisher landing page
- PDF URL
-
https://www.mdpi.com/2072-4292/13/11/2198/pdf?version=1622809631Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2072-4292/13/11/2198/pdf?version=1622809631Direct OA link when available
- Concepts
-
Computer science, Gradient descent, Inverse synthetic aperture radar, Synthetic aperture radar, Entropy (arrow of time), Computer vision, Artificial intelligence, Algorithm, Radar imaging, Radar, Physics, Artificial neural network, Quantum mechanics, TelecommunicationsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
13Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 2, 2023: 6, 2022: 4, 2021: 1Per-year citation counts (last 5 years)
- References (count)
-
34Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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