Evaluation of Application Effectiveness on Ocean Salinity Satellite RFI Detection Algorithms
In order to alleviate the impact of radio frequency interference (RFI) on the accuracy of ocean salinity satellite remote sensing, scholars have proposed various detection and labeling algorithms for RFI based on remote sensing data from the SMOS satellite. However, the signals that generate RFI are...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
American Association for the Advancement of Science (AAAS)
2024-01-01
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| Series: | Space: Science & Technology |
| Online Access: | https://spj.science.org/doi/10.34133/space.0098 |
| _version_ | 1797258730898718720 |
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| author | Liqiang Zhang Qingxia Li Haitao Qiu Qingjun Zhang Yixin Gao Rong Jin Rui Wang Huan Zhang Zhongkai Wen Jian Zhang |
| author_facet | Liqiang Zhang Qingxia Li Haitao Qiu Qingjun Zhang Yixin Gao Rong Jin Rui Wang Huan Zhang Zhongkai Wen Jian Zhang |
| author_sort | Liqiang Zhang |
| collection | DOAJ |
| description | In order to alleviate the impact of radio frequency interference (RFI) on the accuracy of ocean salinity satellite remote sensing, scholars have proposed various detection and labeling algorithms for RFI based on remote sensing data from the SMOS satellite. However, the signals that generate RFI are diverse, and the factors that influence remote sensing observation data are complex. Existing algorithms often target specific hypothetical conditions, lacking general applicability, which frequently leads to an important gap between the nominal performance of the literature and practical applications, posing great challenges to data labeling work. To address this problem, this study conducted a comprehensive and systematic analysis of RFI simulation based on scene modeling, algorithm modeling, and RFI energy modeling. Three typical RFI detection algorithms were selected, and the simulation scene was divided into 3 typical scenes: ocean, land, and sea–land scenes, and RFI was analyzed in terms of weak, moderate, strong, and extremely strong based on energy. Through simulation analysis and evaluation of RFI detection algorithms, lookup tables for algorithm selection, detection rate, and false-positive rate have been established for different intensities of independent RFI sources and multiple nearby RFI sources in the above scenario. These lookup tables have universal guiding significance and provide reliability assurance in complex situations. |
| first_indexed | 2024-04-24T22:58:11Z |
| format | Article |
| id | doaj.art-78a5e037b59345a1ba8c1b73561accb4 |
| institution | Directory Open Access Journal |
| issn | 2692-7659 |
| language | English |
| last_indexed | 2024-04-24T22:58:11Z |
| publishDate | 2024-01-01 |
| publisher | American Association for the Advancement of Science (AAAS) |
| record_format | Article |
| series | Space: Science & Technology |
| spelling | doaj.art-78a5e037b59345a1ba8c1b73561accb42024-03-18T02:35:42ZengAmerican Association for the Advancement of Science (AAAS)Space: Science & Technology2692-76592024-01-01410.34133/space.0098Evaluation of Application Effectiveness on Ocean Salinity Satellite RFI Detection AlgorithmsLiqiang Zhang0Qingxia Li1Haitao Qiu2Qingjun Zhang3Yixin Gao4Rong Jin5Rui Wang6Huan Zhang7Zhongkai Wen8Jian Zhang9School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China.School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China.Aerospace Long-march International Trade Co., Ltd., Beijing 100054, China.Insititute of Remote Sensing Satellite, China Academy of Space Technology, Beijing 100094, China.School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China.School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China.Insititute of Remote Sensing Satellite, China Academy of Space Technology, Beijing 100094, China.Insititute of Remote Sensing Satellite, China Academy of Space Technology, Beijing 100094, China.Insititute of Remote Sensing Satellite, China Academy of Space Technology, Beijing 100094, China.Insititute of Remote Sensing Satellite, China Academy of Space Technology, Beijing 100094, China.In order to alleviate the impact of radio frequency interference (RFI) on the accuracy of ocean salinity satellite remote sensing, scholars have proposed various detection and labeling algorithms for RFI based on remote sensing data from the SMOS satellite. However, the signals that generate RFI are diverse, and the factors that influence remote sensing observation data are complex. Existing algorithms often target specific hypothetical conditions, lacking general applicability, which frequently leads to an important gap between the nominal performance of the literature and practical applications, posing great challenges to data labeling work. To address this problem, this study conducted a comprehensive and systematic analysis of RFI simulation based on scene modeling, algorithm modeling, and RFI energy modeling. Three typical RFI detection algorithms were selected, and the simulation scene was divided into 3 typical scenes: ocean, land, and sea–land scenes, and RFI was analyzed in terms of weak, moderate, strong, and extremely strong based on energy. Through simulation analysis and evaluation of RFI detection algorithms, lookup tables for algorithm selection, detection rate, and false-positive rate have been established for different intensities of independent RFI sources and multiple nearby RFI sources in the above scenario. These lookup tables have universal guiding significance and provide reliability assurance in complex situations.https://spj.science.org/doi/10.34133/space.0098 |
| spellingShingle | Liqiang Zhang Qingxia Li Haitao Qiu Qingjun Zhang Yixin Gao Rong Jin Rui Wang Huan Zhang Zhongkai Wen Jian Zhang Evaluation of Application Effectiveness on Ocean Salinity Satellite RFI Detection Algorithms Space: Science & Technology |
| title | Evaluation of Application Effectiveness on Ocean Salinity Satellite RFI Detection Algorithms |
| title_full | Evaluation of Application Effectiveness on Ocean Salinity Satellite RFI Detection Algorithms |
| title_fullStr | Evaluation of Application Effectiveness on Ocean Salinity Satellite RFI Detection Algorithms |
| title_full_unstemmed | Evaluation of Application Effectiveness on Ocean Salinity Satellite RFI Detection Algorithms |
| title_short | Evaluation of Application Effectiveness on Ocean Salinity Satellite RFI Detection Algorithms |
| title_sort | evaluation of application effectiveness on ocean salinity satellite rfi detection algorithms |
| url | https://spj.science.org/doi/10.34133/space.0098 |
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