Extraction and Mitigation of Radio Frequency Interference Artifacts Based on Time-Series Sentinel-1 SAR Data

Tao, Mingliang; Lai, Siqi; Li, Jieshuang; Su, Jia; Fan, Yifei

doi:10.1109/tgrs.2021.3126485

Cited by 21 publications

(5 citation statements)

References 31 publications

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“…Traditional algorithms mostly utilize level-0 SAR data, specifically in the raw data domain, for interference suppression, without extensively focusing on the characterization of RFI signals. Access to level-0 SAR data is often limited for users, leading them to primarily work with level-1 data [24][25][26][27][28][29]. As a result, it becomes imperative to prioritize the development of interference suppression techniques that are specifically designed for level-1 data.…”

Section: Introductionmentioning

confidence: 99%

A Sparse Recovery Algorithm for Suppressing Multiple Linear Frequency Modulation Interference in the Synthetic Aperture Radar Image Domain

Tong,

Lu,

Yang

et al. 2024

Sensors

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In synthetic aperture radar (SAR) signal processing, compared with the raw data of level-0, level-1 SAR images are more readily accessible and available in larger quantities. However, an amount of level-1 images are affected by radio frequency interference (RFI), which typically originates from Linear Frequency Modulation (LFM) signals emitted by ground-based radars. Existing research on interference suppression in level-1 data has primarily focused on two methods: transforming SAR images into simulated echo data for interference suppression, or focusing interference in the frequency domain and applying notching filters to reduce interference energy. However, these methods overlook the effective utilization of the interference parameters or are confined to suppressing only one type of LFM interference at a time. In certain SAR images, multiple types of LFM interference manifest bright radiation artifacts that exhibit varying lengths along the range direction while remaining constant in the azimuth direction. It is necessary to suppress multiple LFM interference on SAR images when original echo data are unavailable. This article proposes a joint sparse recovery algorithm for interference suppression in the SAR image domain. In the SAR image domain, two-dimensional LFM interference typically exhibits differences in parameters such as frequency modulation rate and pulse width in the range direction, while maintaining consistency in the azimuth direction. Based on this observation, this article constructs a series of focusing operators for LFM interference in SAR images. These operators enable the sparse representation of dispersed LFM interference. Subsequently, an optimization model is developed that can effectively suppress multi-LFM interference and reduce image loss with the assistance of a regularization term in the image domain. Simulation experiments conducted in various scenarios validate the superior performance of the proposed method.

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Section: Introductionmentioning

confidence: 99%

A Sparse Recovery Algorithm for Suppressing Multiple Linear Frequency Modulation Interference in the Synthetic Aperture Radar Image Domain

Tong,

Lu,

Yang

et al. 2024

Sensors

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“…The low-energy RFI can be mitigated because SAR imaging processing has a significant coherent signal-processing gain. However, the high-energy RFI would seriously reduce the image quality and interpretation accuracy of the SAR (e.g., SAR image classification, target detection, and recognition) [5,6]. Meanwhile, RFI would degrade the estimation accuracy of Doppler parameters such as the Doppler center and modulation rate, resulting in unfocused and blurred SAR imaging results [7].…”

Section: Introductionmentioning

confidence: 99%

WBIM-GAN: A Generative Adversarial Network Based Wideband Interference Mitigation Model for Synthetic Aperture Radar

Xu,

Fan,

Wang

et al. 2024

Remote Sensing

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Wideband interference (WBI) can significantly reduce the image quality and interpretation accuracy of synthetic aperture radar (SAR). To eliminate the negative effects of WBI on SAR, we propose a novel end-to-end data-driven approach to mitigate WBI. Specifically, the WBI is mitigated by an explicit function called WBI mitigation–generative adversarial network (WBIM-GAN), mapping from an input WBI-corrupted echo to its properly WBI-free echo. WBIM-GAN comprises a WBI mitigation network and a target echo discriminative network. The WBI mitigation network incorporates a deep residual network to enhance the performance of WBI mitigation while addressing the issue of gradient saturation in the deeper layers. Simultaneously, the class activation mapping technique fully demonstrates that the WBI mitigation network can localize the WBI region rather than the target echo. By utilizing the PatchGAN architecture, the target echo discriminative network can capture the local texture and statistical features of target echoes, thus improving the effectiveness of WBI mitigation. Before applying the WBIM-GAN, the short-time Fourier transform (STFT) converts SAR echoes into a time–frequency domain (TFD) to better characterize WBI features. Finally, by comparing different WBI mitigation methods applied to several real measured SAR data collected by the Sentinel-1 system, the efficiency and superiority of WBIM-GAN are proved sufficiently.

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“…Semi-parametric techniques represent a hybrid approach in statistical modeling and signal processing, combining elements of both parametric and non-parametric methodologies. The authors in [9] developed an adaptive notch semi-parametric method for mitigating strong RFI events in SAR systems, effectively preserving true scene details with adaptive threshold and sparse regularization demonstrating its efficacy through experiments on real SAR data.…”

Section: Introductionmentioning

confidence: 99%

Interference Localization and Mitigation in Synthetic Aperture Radars Using Variable Space-Frequency Filter

Hendy,

Al-Hourani,

Kraus

et al. 2024

Preprint

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Radio Frequency Interference (RFI) in Synthetic Aperture Radar (SAR) is a daunting challenge that affects the SAR sensing reliability and its image quality. To ensure that SAR remains a powerful tool for Earth observation, this paper presents an effective two-dimensional tuneable attenuation space-frequency (azimuth-range) filtration. This framework is based on key components including time-frequency features of level-0 SAR data, radar antenna pattern, and the estimated parameters of the interference signal. Additionally, a signal power localization method is applied to estimate the relative position of the interference source with 95% accuracy to facilitate applying the tuneable filter. Simulated results are obtained using a public open-source spaceborne SAR emulator, SEMUS, for generating emulated clean and contaminated SAR raw data. Furthermore, the filtration framework is successfully tested on real-life interference events on the TerraSAR-X satellite raw data.

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Extraction and Mitigation of Radio Frequency Interference Artifacts Based on Time-Series Sentinel-1 SAR Data

Cited by 21 publications

References 31 publications

A Sparse Recovery Algorithm for Suppressing Multiple Linear Frequency Modulation Interference in the Synthetic Aperture Radar Image Domain

A Sparse Recovery Algorithm for Suppressing Multiple Linear Frequency Modulation Interference in the Synthetic Aperture Radar Image Domain

WBIM-GAN: A Generative Adversarial Network Based Wideband Interference Mitigation Model for Synthetic Aperture Radar

Interference Localization and Mitigation in Synthetic Aperture Radars Using Variable Space-Frequency Filter

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