A Novel Azimuth Channel Errors Estimation Algorithm Based on Characteristic Clusters Statistical Treatment Article Swipe
Wensen Yang
,
Ran Tao
,
Hao Huan
,
Jing Feng
,
Longyong Chen
,
Junhua Yang
,
Yihao Xu
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.20944/preprints202501.0795.v1
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.20944/preprints202501.0795.v1
Azimuth multichannel techniques are promising in the high-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) system. However, in practical engineering, errors among channels will significantly impact the reconstruction of multi-channel echo data, resulting in a smeared SAR image. To address this issue, a novel algorithm is proposed in this article, which based on the statistical treatment of characteristic clusters. In this algorithm, separately channel imaging is carried out firstly, then the image is divided into a certain number of sub-images, then the characteristic clusters and characteristic points in each sub-image are searched, and the positions, amplitude and phase information of the characteristic points are utilized to obtain the range synchronization time errors, amplitude errors and phase errors among channels. Compared with traditional methods, the proposed method do not need to be iterated, nor do they need to solve the complex problem of eigenvalue decomposition of covariance matrix. More gratifying, it can utilize the ready-made imaging tools and software in single channel SAR system. The effectiveness of the proposed method is confirmed by simulation experiments and actual data processing.
Related Topics
Concepts
Computer science
Azimuth
Algorithm
Covariance matrix
Channel (broadcasting)
Synthetic aperture radar
Range (aeronautics)
Amplitude
Phase (matter)
Artificial intelligence
Mathematics
Telecommunications
Materials science
Quantum mechanics
Organic chemistry
Geometry
Physics
Composite material
Chemistry
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.20944/preprints202501.0795.v1
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4406318570
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4406318570Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.20944/preprints202501.0795.v1Digital Object Identifier
- Title
-
A Novel Azimuth Channel Errors Estimation Algorithm Based on Characteristic Clusters Statistical TreatmentWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2025Year of publication
- Publication date
-
2025-01-10Full publication date if available
- Authors
-
Wensen Yang, Ran Tao, Hao Huan, Jing Feng, Longyong Chen, Junhua Yang, Yihao XuList of authors in order
- Landing page
-
https://doi.org/10.20944/preprints202501.0795.v1Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.20944/preprints202501.0795.v1Direct OA link when available
- Concepts
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Computer science, Azimuth, Algorithm, Covariance matrix, Channel (broadcasting), Synthetic aperture radar, Range (aeronautics), Amplitude, Phase (matter), Artificial intelligence, Mathematics, Telecommunications, Materials science, Quantum mechanics, Organic chemistry, Geometry, Physics, Composite material, ChemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.channels. | 118 |
| abstract_inverted_index.clusters. | 58 |
| abstract_inverted_index.confirmed | 170 |
| abstract_inverted_index.iterated, | 131 |
| abstract_inverted_index.practical | 18 |
| abstract_inverted_index.promising | 4 |
| abstract_inverted_index.resulting | 32 |
| abstract_inverted_index.searched, | 91 |
| abstract_inverted_index.sub-image | 89 |
| abstract_inverted_index.synthetic | 11 |
| abstract_inverted_index.treatment | 55 |
| abstract_inverted_index.algorithm, | 61 |
| abstract_inverted_index.covariance | 145 |
| abstract_inverted_index.eigenvalue | 142 |
| abstract_inverted_index.positions, | 94 |
| abstract_inverted_index.ready-made | 153 |
| abstract_inverted_index.separately | 62 |
| abstract_inverted_index.simulation | 172 |
| abstract_inverted_index.techniques | 2 |
| abstract_inverted_index.wide-swath | 9 |
| abstract_inverted_index.experiments | 173 |
| abstract_inverted_index.gratifying, | 148 |
| abstract_inverted_index.information | 98 |
| abstract_inverted_index.processing. | 177 |
| abstract_inverted_index.statistical | 54 |
| abstract_inverted_index.sub-images, | 79 |
| abstract_inverted_index.traditional | 121 |
| abstract_inverted_index.engineering, | 19 |
| abstract_inverted_index.multichannel | 1 |
| abstract_inverted_index.decomposition | 143 |
| abstract_inverted_index.effectiveness | 164 |
| abstract_inverted_index.multi-channel | 29 |
| abstract_inverted_index.significantly | 24 |
| abstract_inverted_index.characteristic | 57, 82, 85, 101 |
| abstract_inverted_index.reconstruction | 27 |
| abstract_inverted_index.high-resolution | 7 |
| abstract_inverted_index.synchronization | 109 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile.value | 0.00547745 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |