Unblurring ISAR Imaging for Maneuvering Target Based on UFGAN
Inverse synthetic aperture radar (ISAR) imaging for maneuvering targets suffers from a Doppler frequency time-varying problem, leading to the ISAR images blurred in the azimuth direction. Given that the traditional imaging methods have poor imaging performance or low efficiency, and the existing dee...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/20/5270 |
| _version_ | 1797470066042732544 |
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| author | Wenzhe Li Yanxin Yuan Yuanpeng Zhang Ying Luo |
| author_facet | Wenzhe Li Yanxin Yuan Yuanpeng Zhang Ying Luo |
| author_sort | Wenzhe Li |
| collection | DOAJ |
| description | Inverse synthetic aperture radar (ISAR) imaging for maneuvering targets suffers from a Doppler frequency time-varying problem, leading to the ISAR images blurred in the azimuth direction. Given that the traditional imaging methods have poor imaging performance or low efficiency, and the existing deep learning imaging methods cannot effectively reconstruct the deblurred ISAR images retaining rich details and textures, an unblurring ISAR imaging method based on an advanced Transformer structure for maneuvering targets is proposed. We first present a pseudo-measured data generation method based on the DeepLabv3+ network and Diamond-Square algorithm to acquire an ISAR dataset for training with good generalization to measured data. Next, with the locally-enhanced window Transformer block adopted to enhance the ability to capture local context as well as global dependencies, we construct a novel Uformer-based GAN (UFGAN) to restore the deblurred ISAR images with rich details and textures from blurred imaging results. The simulation and measured experiments show that the proposed method can achieve fast and high-quality imaging for maneuvering targets under the condition of a low signal-to-noise ratio (SNR) and sparse aperture. |
| first_indexed | 2024-03-09T19:31:32Z |
| format | Article |
| id | doaj.art-40bf05789e114580bcf6eac736ec01f6 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T19:31:32Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-40bf05789e114580bcf6eac736ec01f62023-11-24T02:22:25ZengMDPI AGRemote Sensing2072-42922022-10-011420527010.3390/rs14205270Unblurring ISAR Imaging for Maneuvering Target Based on UFGANWenzhe Li0Yanxin Yuan1Yuanpeng Zhang2Ying Luo3Information and Navigation College, Air Force Engineering University, Xi’an 710077, ChinaInformation and Navigation College, Air Force Engineering University, Xi’an 710077, ChinaInformation and Navigation College, Air Force Engineering University, Xi’an 710077, ChinaInformation and Navigation College, Air Force Engineering University, Xi’an 710077, ChinaInverse synthetic aperture radar (ISAR) imaging for maneuvering targets suffers from a Doppler frequency time-varying problem, leading to the ISAR images blurred in the azimuth direction. Given that the traditional imaging methods have poor imaging performance or low efficiency, and the existing deep learning imaging methods cannot effectively reconstruct the deblurred ISAR images retaining rich details and textures, an unblurring ISAR imaging method based on an advanced Transformer structure for maneuvering targets is proposed. We first present a pseudo-measured data generation method based on the DeepLabv3+ network and Diamond-Square algorithm to acquire an ISAR dataset for training with good generalization to measured data. Next, with the locally-enhanced window Transformer block adopted to enhance the ability to capture local context as well as global dependencies, we construct a novel Uformer-based GAN (UFGAN) to restore the deblurred ISAR images with rich details and textures from blurred imaging results. The simulation and measured experiments show that the proposed method can achieve fast and high-quality imaging for maneuvering targets under the condition of a low signal-to-noise ratio (SNR) and sparse aperture.https://www.mdpi.com/2072-4292/14/20/5270inverse synthetic aperture radar imaging (ISAR)deep learning (DL)deblurringTransformerUformer-based GAN (UFGAN)pseudo-measured data |
| spellingShingle | Wenzhe Li Yanxin Yuan Yuanpeng Zhang Ying Luo Unblurring ISAR Imaging for Maneuvering Target Based on UFGAN Remote Sensing inverse synthetic aperture radar imaging (ISAR) deep learning (DL) deblurring Transformer Uformer-based GAN (UFGAN) pseudo-measured data |
| title | Unblurring ISAR Imaging for Maneuvering Target Based on UFGAN |
| title_full | Unblurring ISAR Imaging for Maneuvering Target Based on UFGAN |
| title_fullStr | Unblurring ISAR Imaging for Maneuvering Target Based on UFGAN |
| title_full_unstemmed | Unblurring ISAR Imaging for Maneuvering Target Based on UFGAN |
| title_short | Unblurring ISAR Imaging for Maneuvering Target Based on UFGAN |
| title_sort | unblurring isar imaging for maneuvering target based on ufgan |
| topic | inverse synthetic aperture radar imaging (ISAR) deep learning (DL) deblurring Transformer Uformer-based GAN (UFGAN) pseudo-measured data |
| url | https://www.mdpi.com/2072-4292/14/20/5270 |
| work_keys_str_mv | AT wenzheli unblurringisarimagingformaneuveringtargetbasedonufgan AT yanxinyuan unblurringisarimagingformaneuveringtargetbasedonufgan AT yuanpengzhang unblurringisarimagingformaneuveringtargetbasedonufgan AT yingluo unblurringisarimagingformaneuveringtargetbasedonufgan |