Mitigate the LFM-PRFI in SAR Data: Joint Down-Range and Cross-Range Filtering

Lv, Zongsen; Fan, Huaitao; Chen, Zhen; Qiu, Jinsong; Ren, Mingshan; Zhang, Zhimin

doi:10.1109/tgrs.2023.3265774

Cited by 10 publications

(2 citation statements)

References 49 publications

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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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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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“…However, in the case of large missing data ratios, the adaptive filling method is hard to recover the notched data. To improve the notch method, Lv et al [3] proposed a linear frequency modulation (LFM) PRFI suppression method in SAR raw data by combining range and azimuth filtering in 2023, which overcomes the abnormal target response introduced by conventional notch filtering methods, and is suitable for LFM PRFI suppression with large bandwidth and pulse width. However, the suppression performance depends on the accuracy of FM rate estimation.…”

Section: Introductionmentioning

confidence: 99%

SAR pulse radio frequency interference suppression based on modified eigen subspace projection

Liu,

Shu,

2023

International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023)

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Synthetic aperture radar (SAR), as an active microwave remote sensor with high-resolution earth observation, is easily affected by radio frequency interference (RFI). Pulse radio frequency interference (PRFI), as a typical form of RFI, pollutes SAR raw data with bright stripes, which increases the difficulty of SAR image interpretation. However, the conventional eigen subspace projection (ESP) method decomposes the whole pulse into the interferences and the useful signals, resulting in the loss of useful signals in the non-interference positions with the pulse. To solve this problem, this paper proposes an improved SAR PRFI suppression method based on ESP. Firstly, the receiving pulses containing PRFI are detected and the specific position of PRFI in the pulse are determined by eigenvalue decomposition and energy accumulation in timefrequency domain, respectively. Secondly, the PRFI signal is separated from the useful signal by applying the ESP method only to the data where the PRFI is located. Finally, the PRFI signal projected by ESP is eliminated from the raw data. Simulation results shown that the proposed method can better protected useful signals and improve SAR image quality compared with conventional methods.

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