Highly Robust Synthetic Aperture Radar Target Recognition Method Based on Simulation Data Training
Sufficient synthetic aperture radar (SAR) data is the key element in achieving excellent target recognition performance for most deep learning algorithms. It is unrealistic to obtain sufficient SAR data from the actual measurements, so SAR simulation based on electromagnetic scattering modeling has...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | International Journal of Antennas and Propagation |
| Online Access: | http://dx.doi.org/10.1155/2022/7537732 |
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| author | Liping Hu Canming Yao Jian Huang Jinfan Liu Guanyong Wang |
| author_facet | Liping Hu Canming Yao Jian Huang Jinfan Liu Guanyong Wang |
| author_sort | Liping Hu |
| collection | DOAJ |
| description | Sufficient synthetic aperture radar (SAR) data is the key element in achieving excellent target recognition performance for most deep learning algorithms. It is unrealistic to obtain sufficient SAR data from the actual measurements, so SAR simulation based on electromagnetic scattering modeling has become an effective way to obtain sufficient samples. Simulated and measured SAR images are nonhomologous data. Due to the fact that the target geometric model of SAR simulation is not inevitably consistent with the real object, the SAR sensor model in SAR simulation may be different from the actual sensor, the background environment of the object is also inevitably different from that of SAR simulation, the error of electromagnetic modeling method itself, and so on. There are inevitable differences between the simulated and measured SAR images, which will affect the recognition performance. To address this problem, an SAR simulation method based on a high-frequency asymptotic technique and a discrete ray tracing technique is proposed in this paper to obtain SAR simulation images of ground vehicle targets. Next, various convolutional neural networks (CNNs) and AugMix data augmentation methods are proposed to train only on simulated data, and then target recognition on MSTAR measured data is performed. The experiments show that all the CNNs can achieve incredible recognition performance on the nonhomologous SAR data, and the RegNetX-3.2GF achieves state-of-the-art performance, up to 84.81%. |
| format | Article |
| id | doaj-art-04251c8490e04334a61ae7bc659b569f |
| institution | Kabale University |
| issn | 1687-5877 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Antennas and Propagation |
| spelling | doaj-art-04251c8490e04334a61ae7bc659b569f2025-08-20T03:54:25ZengWileyInternational Journal of Antennas and Propagation1687-58772022-01-01202210.1155/2022/7537732Highly Robust Synthetic Aperture Radar Target Recognition Method Based on Simulation Data TrainingLiping Hu0Canming Yao1Jian Huang2Jinfan Liu3Guanyong Wang4Science and Technology on Electromagnetic Scattering LaboratorySchool of Electronics and Communication EngineeringBeijing Institute of Tracking and Telecommunications TechnologyScience and Technology on Electromagnetic Scattering LaboratoryBeijing Institute of Radio MeasurementSufficient synthetic aperture radar (SAR) data is the key element in achieving excellent target recognition performance for most deep learning algorithms. It is unrealistic to obtain sufficient SAR data from the actual measurements, so SAR simulation based on electromagnetic scattering modeling has become an effective way to obtain sufficient samples. Simulated and measured SAR images are nonhomologous data. Due to the fact that the target geometric model of SAR simulation is not inevitably consistent with the real object, the SAR sensor model in SAR simulation may be different from the actual sensor, the background environment of the object is also inevitably different from that of SAR simulation, the error of electromagnetic modeling method itself, and so on. There are inevitable differences between the simulated and measured SAR images, which will affect the recognition performance. To address this problem, an SAR simulation method based on a high-frequency asymptotic technique and a discrete ray tracing technique is proposed in this paper to obtain SAR simulation images of ground vehicle targets. Next, various convolutional neural networks (CNNs) and AugMix data augmentation methods are proposed to train only on simulated data, and then target recognition on MSTAR measured data is performed. The experiments show that all the CNNs can achieve incredible recognition performance on the nonhomologous SAR data, and the RegNetX-3.2GF achieves state-of-the-art performance, up to 84.81%.http://dx.doi.org/10.1155/2022/7537732 |
| spellingShingle | Liping Hu Canming Yao Jian Huang Jinfan Liu Guanyong Wang Highly Robust Synthetic Aperture Radar Target Recognition Method Based on Simulation Data Training International Journal of Antennas and Propagation |
| title | Highly Robust Synthetic Aperture Radar Target Recognition Method Based on Simulation Data Training |
| title_full | Highly Robust Synthetic Aperture Radar Target Recognition Method Based on Simulation Data Training |
| title_fullStr | Highly Robust Synthetic Aperture Radar Target Recognition Method Based on Simulation Data Training |
| title_full_unstemmed | Highly Robust Synthetic Aperture Radar Target Recognition Method Based on Simulation Data Training |
| title_short | Highly Robust Synthetic Aperture Radar Target Recognition Method Based on Simulation Data Training |
| title_sort | highly robust synthetic aperture radar target recognition method based on simulation data training |
| url | http://dx.doi.org/10.1155/2022/7537732 |
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