A Radio Frequency Interference Screening Framework—From Quick-Look Detection Using Statistics-Assisted Network to Raw Echo Tracing
Synthetic aperture radar (SAR) is often affected by other high-power electromagnetic devices during ground observation, which causes unintentional radio frequency interference (RFI) with the acquired echo, bringing adverse effects into data processing and image interpretation. When faced with the ta...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-11-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/16/22/4195 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846152542343397376 |
|---|---|
| author | Jiayuan Shen Bing Han Yang Li Zongxu Pan Di Yin Yugang Feng Guangzuo Li |
| author_facet | Jiayuan Shen Bing Han Yang Li Zongxu Pan Di Yin Yugang Feng Guangzuo Li |
| author_sort | Jiayuan Shen |
| collection | DOAJ |
| description | Synthetic aperture radar (SAR) is often affected by other high-power electromagnetic devices during ground observation, which causes unintentional radio frequency interference (RFI) with the acquired echo, bringing adverse effects into data processing and image interpretation. When faced with the task of screening massive SAR data, there is an urgent need for the global perception and detection of interference. The existing RFI detection method usually only uses a single type of data for detection, ignoring the information association between the data at all levels of the real SAR product, resulting in some computational redundancy. Meanwhile, current deep learning-based algorithms are often unable to locate the range of RFI coverage in the azimuth direction. Therefore, a novel RFI processing framework from quick-looks to single-look complex (SLC) data and then to raw echo is proposed. We take the data of Sentinel-1 terrain observation with progressive scan (TOPS) mode as an example. By combining the statistics-assisted network with the sliding-window algorithm and the error-tolerant training strategy, it is possible to accurately detect and locate RFI in the quick looks of an SLC product. Then, through the analysis of the TOPSAR imaging principle, the position of the RFI in the SLC image is preliminarily confirmed. The possible distribution of the RFI in the corresponding raw echo is further inferred, which is one of the first attempts to use spaceborne SAR data to elucidate the RFI location mapping relationship between image data and raw echo. Compared with directly detecting all of the SLC data, the time for the proposed framework to determine the RFI distribution in the SLC data can be shortened by 53.526%. All the research in this paper is conducted on Sentinel-1 real data, which verify the feasibility and effectiveness of the proposed framework for radio frequency signals monitoring in advanced spaceborne SAR systems. |
| format | Article |
| id | doaj-art-0eb2a86ebd004e4b9a2fb243034d866c |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-0eb2a86ebd004e4b9a2fb243034d866c2024-11-26T18:19:56ZengMDPI AGRemote Sensing2072-42922024-11-011622419510.3390/rs16224195A Radio Frequency Interference Screening Framework—From Quick-Look Detection Using Statistics-Assisted Network to Raw Echo TracingJiayuan Shen0Bing Han1Yang Li2Zongxu Pan3Di Yin4Yugang Feng5Guangzuo Li6Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaSchool of Information Science and Technology, North China University of Technology, Beijing 100144, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaSynthetic aperture radar (SAR) is often affected by other high-power electromagnetic devices during ground observation, which causes unintentional radio frequency interference (RFI) with the acquired echo, bringing adverse effects into data processing and image interpretation. When faced with the task of screening massive SAR data, there is an urgent need for the global perception and detection of interference. The existing RFI detection method usually only uses a single type of data for detection, ignoring the information association between the data at all levels of the real SAR product, resulting in some computational redundancy. Meanwhile, current deep learning-based algorithms are often unable to locate the range of RFI coverage in the azimuth direction. Therefore, a novel RFI processing framework from quick-looks to single-look complex (SLC) data and then to raw echo is proposed. We take the data of Sentinel-1 terrain observation with progressive scan (TOPS) mode as an example. By combining the statistics-assisted network with the sliding-window algorithm and the error-tolerant training strategy, it is possible to accurately detect and locate RFI in the quick looks of an SLC product. Then, through the analysis of the TOPSAR imaging principle, the position of the RFI in the SLC image is preliminarily confirmed. The possible distribution of the RFI in the corresponding raw echo is further inferred, which is one of the first attempts to use spaceborne SAR data to elucidate the RFI location mapping relationship between image data and raw echo. Compared with directly detecting all of the SLC data, the time for the proposed framework to determine the RFI distribution in the SLC data can be shortened by 53.526%. All the research in this paper is conducted on Sentinel-1 real data, which verify the feasibility and effectiveness of the proposed framework for radio frequency signals monitoring in advanced spaceborne SAR systems.https://www.mdpi.com/2072-4292/16/22/4195synthetic aperture radar (SAR)radio frequency interference (RFI)interference detectiondeep neural networkimage histograminterference tracing |
| spellingShingle | Jiayuan Shen Bing Han Yang Li Zongxu Pan Di Yin Yugang Feng Guangzuo Li A Radio Frequency Interference Screening Framework—From Quick-Look Detection Using Statistics-Assisted Network to Raw Echo Tracing Remote Sensing synthetic aperture radar (SAR) radio frequency interference (RFI) interference detection deep neural network image histogram interference tracing |
| title | A Radio Frequency Interference Screening Framework—From Quick-Look Detection Using Statistics-Assisted Network to Raw Echo Tracing |
| title_full | A Radio Frequency Interference Screening Framework—From Quick-Look Detection Using Statistics-Assisted Network to Raw Echo Tracing |
| title_fullStr | A Radio Frequency Interference Screening Framework—From Quick-Look Detection Using Statistics-Assisted Network to Raw Echo Tracing |
| title_full_unstemmed | A Radio Frequency Interference Screening Framework—From Quick-Look Detection Using Statistics-Assisted Network to Raw Echo Tracing |
| title_short | A Radio Frequency Interference Screening Framework—From Quick-Look Detection Using Statistics-Assisted Network to Raw Echo Tracing |
| title_sort | radio frequency interference screening framework from quick look detection using statistics assisted network to raw echo tracing |
| topic | synthetic aperture radar (SAR) radio frequency interference (RFI) interference detection deep neural network image histogram interference tracing |
| url | https://www.mdpi.com/2072-4292/16/22/4195 |
| work_keys_str_mv | AT jiayuanshen aradiofrequencyinterferencescreeningframeworkfromquicklookdetectionusingstatisticsassistednetworktorawechotracing AT binghan aradiofrequencyinterferencescreeningframeworkfromquicklookdetectionusingstatisticsassistednetworktorawechotracing AT yangli aradiofrequencyinterferencescreeningframeworkfromquicklookdetectionusingstatisticsassistednetworktorawechotracing AT zongxupan aradiofrequencyinterferencescreeningframeworkfromquicklookdetectionusingstatisticsassistednetworktorawechotracing AT diyin aradiofrequencyinterferencescreeningframeworkfromquicklookdetectionusingstatisticsassistednetworktorawechotracing AT yugangfeng aradiofrequencyinterferencescreeningframeworkfromquicklookdetectionusingstatisticsassistednetworktorawechotracing AT guangzuoli aradiofrequencyinterferencescreeningframeworkfromquicklookdetectionusingstatisticsassistednetworktorawechotracing AT jiayuanshen radiofrequencyinterferencescreeningframeworkfromquicklookdetectionusingstatisticsassistednetworktorawechotracing AT binghan radiofrequencyinterferencescreeningframeworkfromquicklookdetectionusingstatisticsassistednetworktorawechotracing AT yangli radiofrequencyinterferencescreeningframeworkfromquicklookdetectionusingstatisticsassistednetworktorawechotracing AT zongxupan radiofrequencyinterferencescreeningframeworkfromquicklookdetectionusingstatisticsassistednetworktorawechotracing AT diyin radiofrequencyinterferencescreeningframeworkfromquicklookdetectionusingstatisticsassistednetworktorawechotracing AT yugangfeng radiofrequencyinterferencescreeningframeworkfromquicklookdetectionusingstatisticsassistednetworktorawechotracing AT guangzuoli radiofrequencyinterferencescreeningframeworkfromquicklookdetectionusingstatisticsassistednetworktorawechotracing |