ISAR cross-range scaling based on the MUSIC technique Article Swipe
Qiuchen Liu
,
Yong Wang
,
Qingxiang Zhang
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.23919/jsee.2020.000070
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.23919/jsee.2020.000070
Cross-range scaling plays an important role in the inverse synthetic aperture radar (ISAR) imaging.Many of the published cross-range scaling algorithms are based on the fast Fourier transformation (FFT).However, the FFT technique is resolution limited, so that the FFT-based algorithms will fail in the rotation velocity (RV) estimation of the slow rotation target.In this paper, we propose an accurate cross-range scaling algorithm based on the multiple signal classification (MUSIC) method.We first select some range bins with the mono-component linear frequency modulated (LFM) signal model.Then, we dechirp the signal of each selected range bin into the form of sinusoidal signal, and utilize the super-resolution MUSIC technique to accurately estimate the frequency.After processing all the range bins, a linear relationship related to the RV can be obtained.Eventually, the ISAR image can be scaled.The proposal can precisely estimate the small RV of the slow rotation target with low computational complexity.Furthermore, the proposal can also be used in the case of cross-range scaling for the sparse aperture data.Experimental results with the simulated and raw data validate the superiority of the novel method.
Related Topics
Concepts
Inverse synthetic aperture radar
Fast Fourier transform
Computer science
Algorithm
Scaling
Range (aeronautics)
SIGNAL (programming language)
Rotation (mathematics)
Synthetic aperture radar
Radar
Artificial intelligence
Radar imaging
Mathematics
Telecommunications
Geometry
Materials science
Programming language
Composite material
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.23919/jsee.2020.000070
- https://ieeexplore.ieee.org/ielx7/5971804/9247409/09247417.pdf
- OA Status
- diamond
- Cited By
- 7
- References
- 28
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3094646665
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3094646665Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.23919/jsee.2020.000070Digital Object Identifier
- Title
-
ISAR cross-range scaling based on the MUSIC techniqueWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-10-01Full publication date if available
- Authors
-
Qiuchen Liu, Yong Wang, Qingxiang ZhangList of authors in order
- Landing page
-
https://doi.org/10.23919/jsee.2020.000070Publisher landing page
- PDF URL
-
https://ieeexplore.ieee.org/ielx7/5971804/9247409/09247417.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://ieeexplore.ieee.org/ielx7/5971804/9247409/09247417.pdfDirect OA link when available
- Concepts
-
Inverse synthetic aperture radar, Fast Fourier transform, Computer science, Algorithm, Scaling, Range (aeronautics), SIGNAL (programming language), Rotation (mathematics), Synthetic aperture radar, Radar, Artificial intelligence, Radar imaging, Mathematics, Telecommunications, Geometry, Materials science, Programming language, Composite materialTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
7Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 2, 2023: 3, 2022: 2Per-year citation counts (last 5 years)
- References (count)
-
28Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.we | 54, 83 |
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| abstract_inverted_index.and | 98, 167 |
| abstract_inverted_index.are | 20 |
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| abstract_inverted_index.(LFM) | 80 |
| abstract_inverted_index.MUSIC | 102 |
| abstract_inverted_index.based | 21, 61 |
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| abstract_inverted_index.first | 69 |
| abstract_inverted_index.image | 126 |
| abstract_inverted_index.novel | 175 |
| abstract_inverted_index.plays | 2 |
| abstract_inverted_index.radar | 11 |
| abstract_inverted_index.range | 72, 90, 112 |
| abstract_inverted_index.small | 135 |
| abstract_inverted_index.(ISAR) | 12 |
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| abstract_inverted_index.select | 70 |
| abstract_inverted_index.signal | 65, 81, 86 |
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| abstract_inverted_index.dechirp | 84 |
| abstract_inverted_index.inverse | 8 |
| abstract_inverted_index.method. | 176 |
| abstract_inverted_index.propose | 55 |
| abstract_inverted_index.related | 117 |
| abstract_inverted_index.results | 163 |
| abstract_inverted_index.scaling | 1, 18, 59, 157 |
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| abstract_inverted_index.accurate | 57 |
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| abstract_inverted_index.selected | 89 |
| abstract_inverted_index.validate | 170 |
| abstract_inverted_index.velocity | 44 |
| abstract_inverted_index.FFT-based | 37 |
| abstract_inverted_index.algorithm | 60 |
| abstract_inverted_index.frequency | 78 |
| abstract_inverted_index.important | 4 |
| abstract_inverted_index.method.We | 68 |
| abstract_inverted_index.modulated | 79 |
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| abstract_inverted_index.published | 16 |
| abstract_inverted_index.simulated | 166 |
| abstract_inverted_index.synthetic | 9 |
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| abstract_inverted_index.estimation | 46 |
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| abstract_inverted_index.resolution | 32 |
| abstract_inverted_index.scaled.The | 129 |
| abstract_inverted_index.sinusoidal | 96 |
| abstract_inverted_index.Cross-range | 0 |
| abstract_inverted_index.cross-range | 17, 58, 156 |
| abstract_inverted_index.model.Then, | 82 |
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| abstract_inverted_index.frequency.After | 108 |
| abstract_inverted_index.super-resolution | 101 |
| abstract_inverted_index.data.Experimental | 162 |
| abstract_inverted_index.obtained.Eventually, | 123 |
| abstract_inverted_index.complexity.Furthermore, | 145 |
| cited_by_percentile_year.max | 97 |
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| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.86855852 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |