Narrow-Band RFI Mitigation in Synthetic Aperture Radars Using Variable Space-Frequency Filter

Nermine Hendy;Akram Al-Hourani;Thomas Kraus;Maximilian Schandri;Markus Bachmann;Haytham M. Fayek
{"title":"Narrow-Band RFI Mitigation in Synthetic Aperture Radars Using Variable Space-Frequency Filter","authors":"Nermine Hendy;Akram Al-Hourani;Thomas Kraus;Maximilian Schandri;Markus Bachmann;Haytham M. Fayek","doi":"10.1109/LGRS.2024.3496753","DOIUrl":null,"url":null,"abstract":"Radio frequency interference (RFI) in synthetic aperture radar (SAR) is a daunting challenge, affecting both sensing reliability and image quality. To ensure that SAR remains a powerful tool for Earth observation, this letter presents a 2-D variable attenuation space (azimuth)-frequency filtration (VASFF) method. This framework leverages the time-frequency characteristics of Level-0 SAR data, the RFI power profile, estimated RFI signal parameters, and the SAR antenna pattern to design a novel variable filter. Signal power localization estimates the interference source’s relative position, facilitating filter application. Simulated results, obtained using our open-source emulator, SEMUS, to generate both clean and interference-contaminated raw SAR data, demonstrate that the proposed filter achieves a 2 dB improvement over traditional notch filtering. The framework is further tested on real-life interference events on TerraSAR-X revealing previously obscured image details, validating the framework’s effectiveness.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":0.0000,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10750832/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Radio frequency interference (RFI) in synthetic aperture radar (SAR) is a daunting challenge, affecting both sensing reliability and image quality. To ensure that SAR remains a powerful tool for Earth observation, this letter presents a 2-D variable attenuation space (azimuth)-frequency filtration (VASFF) method. This framework leverages the time-frequency characteristics of Level-0 SAR data, the RFI power profile, estimated RFI signal parameters, and the SAR antenna pattern to design a novel variable filter. Signal power localization estimates the interference source’s relative position, facilitating filter application. Simulated results, obtained using our open-source emulator, SEMUS, to generate both clean and interference-contaminated raw SAR data, demonstrate that the proposed filter achieves a 2 dB improvement over traditional notch filtering. The framework is further tested on real-life interference events on TerraSAR-X revealing previously obscured image details, validating the framework’s effectiveness.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
利用可变空频滤波器缓解合成孔径雷达中的窄带射频干扰
合成孔径雷达(SAR)中的射频干扰(RFI)是一项艰巨的挑战,会影响传感可靠性和图像质量。为确保合成孔径雷达继续成为地球观测的有力工具,本文提出了一种二维可变衰减空间(方位角)-频率滤波(VASFF)方法。该框架利用零级合成孔径雷达数据的时频特征、射频干扰功率曲线、估计的射频干扰信号参数和合成孔径雷达天线模式来设计新型可变滤波器。信号功率定位可估算干扰源的相对位置,从而方便滤波器的应用。使用我们的开源仿真器 SEMUS 生成干净和受干扰污染的原始合成孔径雷达数据所获得的仿真结果表明,所提出的滤波器比传统的陷波滤波器提高了 2 dB。该框架在 TerraSAR-X 的真实干扰事件中进行了进一步测试,揭示了之前被遮挡的图像细节,验证了该框架的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Deeper and Broader Multimodal Fusion: Cascaded Forest-of-Experts for Land Cover Classification Impact of Targeted Sounding Observations From FY-4B GIIRS on Two Super Typhoon Forecasts in 2024 Structural Representation-Guided GAN for Remote Sensing Image Cloud Removal Multispectral Airborne LiDAR Point Cloud Classification With Maximum Entropy Hierarchical Pooling A Satellite Selection Algorithm for GNSS-R InSAR Elevation Deformation Retrieval
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1