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Near-Field High-Resolution SAR Imaging with Sparse Sampling Interval

Near-field high-resolution synthetic aperture radar (SAR) imaging is mostly accompanied by a large number of data acquisition processes, which increases the system complexity and device cost. According to extensive reports, reducing the number of sampling points of a radar in space can greatly reduc...

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Autores principales: Zhao, Chengyi, Xu, Leijun, Bai, Xue, Chen, Jianfeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9330117/
https://www.ncbi.nlm.nih.gov/pubmed/35898051
http://dx.doi.org/10.3390/s22155548
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author Zhao, Chengyi
Xu, Leijun
Bai, Xue
Chen, Jianfeng
author_facet Zhao, Chengyi
Xu, Leijun
Bai, Xue
Chen, Jianfeng
author_sort Zhao, Chengyi
collection PubMed
description Near-field high-resolution synthetic aperture radar (SAR) imaging is mostly accompanied by a large number of data acquisition processes, which increases the system complexity and device cost. According to extensive reports, reducing the number of sampling points of a radar in space can greatly reduce the amount of data. However, when spatial sparse sampling is carried out, a ghost normally appears in the imaging results due to the high side lobes generated in the azimuth. To address this issue, a technique is introduced in this paper to recover the blank data through amplitude and phase compensation based on the correlation between sparse array sampling through adjacent points. Firstly, the data sampled by the sparse array is compressed in the range direction to obtain the expected data slices in the same range direction. Then, the blank element of the slice is compensated for with amplitude and phase to obtain full aperture data. Finally, the matched filter method is used to aid in the image reconstruction. The simulation results verified that the method proposed in this paper can effectively reconstruct the image under two kinds of sparse sampling conditions. Thus, a simple single-input single-output (SISO) synthetic aperture radar imaging test bench is established. Compared with the results of a 1 mm (1/4 [Formula: see text]) sampling interval, the quality of the reconstructed image under the condition of a 4 mm (1 [Formula: see text]) sampling interval still stands using our proposed method. Demonstrated by the experiment, the normalized root-mean-square error(NMSE) is 5.75%. Additionally, when the spatial sampling points are sampled randomly with 30% of the full sampling condition, this method can also restore and reconstruct the image with high quality. Due to the decrease of sampling points, the data volume can be reduced, which is beneficial for improving the scanning speed and alleviating the pressure of data transmission for near-field high resolution SAR imaging systems.
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spelling pubmed-93301172022-07-29 Near-Field High-Resolution SAR Imaging with Sparse Sampling Interval Zhao, Chengyi Xu, Leijun Bai, Xue Chen, Jianfeng Sensors (Basel) Article Near-field high-resolution synthetic aperture radar (SAR) imaging is mostly accompanied by a large number of data acquisition processes, which increases the system complexity and device cost. According to extensive reports, reducing the number of sampling points of a radar in space can greatly reduce the amount of data. However, when spatial sparse sampling is carried out, a ghost normally appears in the imaging results due to the high side lobes generated in the azimuth. To address this issue, a technique is introduced in this paper to recover the blank data through amplitude and phase compensation based on the correlation between sparse array sampling through adjacent points. Firstly, the data sampled by the sparse array is compressed in the range direction to obtain the expected data slices in the same range direction. Then, the blank element of the slice is compensated for with amplitude and phase to obtain full aperture data. Finally, the matched filter method is used to aid in the image reconstruction. The simulation results verified that the method proposed in this paper can effectively reconstruct the image under two kinds of sparse sampling conditions. Thus, a simple single-input single-output (SISO) synthetic aperture radar imaging test bench is established. Compared with the results of a 1 mm (1/4 [Formula: see text]) sampling interval, the quality of the reconstructed image under the condition of a 4 mm (1 [Formula: see text]) sampling interval still stands using our proposed method. Demonstrated by the experiment, the normalized root-mean-square error(NMSE) is 5.75%. Additionally, when the spatial sampling points are sampled randomly with 30% of the full sampling condition, this method can also restore and reconstruct the image with high quality. Due to the decrease of sampling points, the data volume can be reduced, which is beneficial for improving the scanning speed and alleviating the pressure of data transmission for near-field high resolution SAR imaging systems. MDPI 2022-07-25 /pmc/articles/PMC9330117/ /pubmed/35898051 http://dx.doi.org/10.3390/s22155548 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zhao, Chengyi
Xu, Leijun
Bai, Xue
Chen, Jianfeng
Near-Field High-Resolution SAR Imaging with Sparse Sampling Interval
title Near-Field High-Resolution SAR Imaging with Sparse Sampling Interval
title_full Near-Field High-Resolution SAR Imaging with Sparse Sampling Interval
title_fullStr Near-Field High-Resolution SAR Imaging with Sparse Sampling Interval
title_full_unstemmed Near-Field High-Resolution SAR Imaging with Sparse Sampling Interval
title_short Near-Field High-Resolution SAR Imaging with Sparse Sampling Interval
title_sort near-field high-resolution sar imaging with sparse sampling interval
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9330117/
https://www.ncbi.nlm.nih.gov/pubmed/35898051
http://dx.doi.org/10.3390/s22155548
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