DU-CG-STAP Method Based on Sparse Recovery and Unsupervised Learning for Airborne Radar Clutter Suppression
With a small number of training range cells, sparse recovery (SR)-based space–time adaptive processing (STAP) methods can help to suppress clutter and detect targets effectively for airborne radar. However, SR algorithms usually have problems of high computational complexity and parameter-setting di...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/14/3472 |
| _version_ | 1797443948828950528 |
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| author | Bo Zou Xin Wang Weike Feng Hangui Zhu Fuyu Lu |
| author_facet | Bo Zou Xin Wang Weike Feng Hangui Zhu Fuyu Lu |
| author_sort | Bo Zou |
| collection | DOAJ |
| description | With a small number of training range cells, sparse recovery (SR)-based space–time adaptive processing (STAP) methods can help to suppress clutter and detect targets effectively for airborne radar. However, SR algorithms usually have problems of high computational complexity and parameter-setting difficulties. More importantly, non-ideal factors in practice will lead to the degraded clutter suppression performance of SR-STAP methods. Based on the idea of deep unfolding (DU), a space–time two-dimensional (2D)-decoupled SR network, namely 2DMA-Net, is constructed in this paper to achieve a fast clutter spectrum estimation without complicated parameter tuning. For 2DMA-Net, without using labeled data, a self-supervised training method based on raw radar data is implemented. Then, to filter out the interferences caused by non-ideal factors, a cycle-consistent adversarial network (CycleGAN) is used as the image enhancement process for the clutter spectrum obtained using 2DMA-Net. For CycleGAN, an unsupervised training method based on unpaired data is implemented. Finally, 2DMA-Net and CycleGAN are cascaded to achieve a fast and accurate estimation of the clutter spectrum, resulting in the DU-CG-STAP method with unsupervised learning, as demonstrated in this paper. The simulation results show that, compared to existing typical SR-STAP methods, the proposed method can simultaneously improve clutter suppression performance and reduce computational complexity. |
| first_indexed | 2024-03-09T13:04:29Z |
| format | Article |
| id | doaj.art-26da5958f6554788b9e382b14e6900e3 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T13:04:29Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-26da5958f6554788b9e382b14e6900e32023-11-30T21:49:55ZengMDPI AGRemote Sensing2072-42922022-07-011414347210.3390/rs14143472DU-CG-STAP Method Based on Sparse Recovery and Unsupervised Learning for Airborne Radar Clutter SuppressionBo Zou0Xin Wang1Weike Feng2Hangui Zhu3Fuyu Lu4Air and Missile Defense College, Air Force Engineering University, Xi’an 710051, ChinaAir and Missile Defense College, Air Force Engineering University, Xi’an 710051, ChinaAir and Missile Defense College, Air Force Engineering University, Xi’an 710051, ChinaAir and Missile Defense College, Air Force Engineering University, Xi’an 710051, ChinaAir and Missile Defense College, Air Force Engineering University, Xi’an 710051, ChinaWith a small number of training range cells, sparse recovery (SR)-based space–time adaptive processing (STAP) methods can help to suppress clutter and detect targets effectively for airborne radar. However, SR algorithms usually have problems of high computational complexity and parameter-setting difficulties. More importantly, non-ideal factors in practice will lead to the degraded clutter suppression performance of SR-STAP methods. Based on the idea of deep unfolding (DU), a space–time two-dimensional (2D)-decoupled SR network, namely 2DMA-Net, is constructed in this paper to achieve a fast clutter spectrum estimation without complicated parameter tuning. For 2DMA-Net, without using labeled data, a self-supervised training method based on raw radar data is implemented. Then, to filter out the interferences caused by non-ideal factors, a cycle-consistent adversarial network (CycleGAN) is used as the image enhancement process for the clutter spectrum obtained using 2DMA-Net. For CycleGAN, an unsupervised training method based on unpaired data is implemented. Finally, 2DMA-Net and CycleGAN are cascaded to achieve a fast and accurate estimation of the clutter spectrum, resulting in the DU-CG-STAP method with unsupervised learning, as demonstrated in this paper. The simulation results show that, compared to existing typical SR-STAP methods, the proposed method can simultaneously improve clutter suppression performance and reduce computational complexity.https://www.mdpi.com/2072-4292/14/14/3472space–time adaptive processing (STAP)sparse recovery (SR)deep unfolding (DU)cycle-consistent adversarial network (CycleGAN)unsupervised learning |
| spellingShingle | Bo Zou Xin Wang Weike Feng Hangui Zhu Fuyu Lu DU-CG-STAP Method Based on Sparse Recovery and Unsupervised Learning for Airborne Radar Clutter Suppression Remote Sensing space–time adaptive processing (STAP) sparse recovery (SR) deep unfolding (DU) cycle-consistent adversarial network (CycleGAN) unsupervised learning |
| title | DU-CG-STAP Method Based on Sparse Recovery and Unsupervised Learning for Airborne Radar Clutter Suppression |
| title_full | DU-CG-STAP Method Based on Sparse Recovery and Unsupervised Learning for Airborne Radar Clutter Suppression |
| title_fullStr | DU-CG-STAP Method Based on Sparse Recovery and Unsupervised Learning for Airborne Radar Clutter Suppression |
| title_full_unstemmed | DU-CG-STAP Method Based on Sparse Recovery and Unsupervised Learning for Airborne Radar Clutter Suppression |
| title_short | DU-CG-STAP Method Based on Sparse Recovery and Unsupervised Learning for Airborne Radar Clutter Suppression |
| title_sort | du cg stap method based on sparse recovery and unsupervised learning for airborne radar clutter suppression |
| topic | space–time adaptive processing (STAP) sparse recovery (SR) deep unfolding (DU) cycle-consistent adversarial network (CycleGAN) unsupervised learning |
| url | https://www.mdpi.com/2072-4292/14/14/3472 |
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