Multipath and Deep Learning-Based Detection of Ultra-Low Moving Targets Above the Sea
An intelligent approach is proposed and investigated in this paper for the detection of ultra-low-altitude sea-skimming moving targets for airborne pulse Doppler radar. Without suppressing interferences, the proposed method uses both target and multipath information for detection based on their dist...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/24/4773 |
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| _version_ | 1846102865642258432 |
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| author | Zhaolong Wang Xiaokuan Zhang Weike Feng Binfeng Zong Tong Wang Cheng Qi Xixi Chen |
| author_facet | Zhaolong Wang Xiaokuan Zhang Weike Feng Binfeng Zong Tong Wang Cheng Qi Xixi Chen |
| author_sort | Zhaolong Wang |
| collection | DOAJ |
| description | An intelligent approach is proposed and investigated in this paper for the detection of ultra-low-altitude sea-skimming moving targets for airborne pulse Doppler radar. Without suppressing interferences, the proposed method uses both target and multipath information for detection based on their distinguishable image features and deep learning (DL) techniques. First, the image features of the target, multipath, and sea clutter in the real-measured range-Doppler (RD) map are analyzed, based on which the target and multipath are defined together as the generalized target. Then, based on the composite electromagnetic scattering mechanism of the target and the ocean surface, a scattering-based echo generation model is established and validated to generate sufficient data for DL network training. Finally, the RD features of the generalized target are learned by training the DL-based target detector, such as you-only-look-once version 7 (YOLOv7) and Faster R-CNN. The detection results show the high performance of the proposed method on both simulated and real-measured data without suppressing interferences (e.g., clutter, jamming, and noise). In particular, even if the target is submerged in clutter, the target can still be detected by the proposed method based on the multipath feature. |
| format | Article |
| id | doaj-art-8d6951a913584a9ba9dd9f6eab1c8d91 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-8d6951a913584a9ba9dd9f6eab1c8d912024-12-27T14:51:11ZengMDPI AGRemote Sensing2072-42922024-12-011624477310.3390/rs16244773Multipath and Deep Learning-Based Detection of Ultra-Low Moving Targets Above the SeaZhaolong Wang0Xiaokuan Zhang1Weike Feng2Binfeng Zong3Tong Wang4Cheng Qi5Xixi Chen6Air Defense and Antimissile School, Air Force Engineering University, Xi’an 710051, ChinaAir Defense and Antimissile School, Air Force Engineering University, Xi’an 710051, ChinaAir Defense and Antimissile School, Air Force Engineering University, Xi’an 710051, ChinaAir Defense and Antimissile School, Air Force Engineering University, Xi’an 710051, ChinaAir Defense and Antimissile School, Air Force Engineering University, Xi’an 710051, ChinaAir Defense and Antimissile School, Air Force Engineering University, Xi’an 710051, ChinaAir Defense and Antimissile School, Air Force Engineering University, Xi’an 710051, ChinaAn intelligent approach is proposed and investigated in this paper for the detection of ultra-low-altitude sea-skimming moving targets for airborne pulse Doppler radar. Without suppressing interferences, the proposed method uses both target and multipath information for detection based on their distinguishable image features and deep learning (DL) techniques. First, the image features of the target, multipath, and sea clutter in the real-measured range-Doppler (RD) map are analyzed, based on which the target and multipath are defined together as the generalized target. Then, based on the composite electromagnetic scattering mechanism of the target and the ocean surface, a scattering-based echo generation model is established and validated to generate sufficient data for DL network training. Finally, the RD features of the generalized target are learned by training the DL-based target detector, such as you-only-look-once version 7 (YOLOv7) and Faster R-CNN. The detection results show the high performance of the proposed method on both simulated and real-measured data without suppressing interferences (e.g., clutter, jamming, and noise). In particular, even if the target is submerged in clutter, the target can still be detected by the proposed method based on the multipath feature.https://www.mdpi.com/2072-4292/16/24/4773airborne radardeep learningmoving target detectionmultipath featurerange-Doppler map |
| spellingShingle | Zhaolong Wang Xiaokuan Zhang Weike Feng Binfeng Zong Tong Wang Cheng Qi Xixi Chen Multipath and Deep Learning-Based Detection of Ultra-Low Moving Targets Above the Sea Remote Sensing airborne radar deep learning moving target detection multipath feature range-Doppler map |
| title | Multipath and Deep Learning-Based Detection of Ultra-Low Moving Targets Above the Sea |
| title_full | Multipath and Deep Learning-Based Detection of Ultra-Low Moving Targets Above the Sea |
| title_fullStr | Multipath and Deep Learning-Based Detection of Ultra-Low Moving Targets Above the Sea |
| title_full_unstemmed | Multipath and Deep Learning-Based Detection of Ultra-Low Moving Targets Above the Sea |
| title_short | Multipath and Deep Learning-Based Detection of Ultra-Low Moving Targets Above the Sea |
| title_sort | multipath and deep learning based detection of ultra low moving targets above the sea |
| topic | airborne radar deep learning moving target detection multipath feature range-Doppler map |
| url | https://www.mdpi.com/2072-4292/16/24/4773 |
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