Radio Frequency Interference Mitigation for Synthetic Aperture Radar Based on the Time-Frequency Constraint Joint Low-Rank and Sparsity Properties

Ding, Y.; Fan, Weiwei; Zhang, Zijing; Zhou, Feng; Lu, Bingbing

doi:10.3390/rs14030775

Cited by 10 publications

(4 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [47], a graph Laplacian clustering algorithm was proposed to mitigate RFI. In [48], a mitigation algorithm of RFI that combines low-rank and double-sparse features was proposed based on RFI's low-rank and sparse features.…”

Section: Semi-parametric Methodsmentioning

confidence: 99%

An Improved RFI Mitigation Approach for SAR Based on Low-Rank Sparse Decomposition: From the Perspective of Useful Signal Protection

Zhang

Lin

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

As an open system, synthetic aperture radar (SAR) inevitably receives radio frequency interference (RFI) generated by electromagnetic equipment in the same band. The existence of RFI seriously affects SAR signal processing and image interpretation. In recent years, many algorithms and models related to RFI mitigation have been proposed. However, most of that focus on effectively mitigating the RFI is insufficient to protect the useful signals. This article proposes a mitigation method of RFI with a signal-protected capability. (1) The kurtosis coefficient is used to detect RFI pulse-by-pulse, and the echoes containing RFI are stored in matrix form. (2) The preliminary extraction of RFI is complete by low-rank sparse decomposition of the echo matrix containing RFI. (3) For the secondary separation of RFI, the accurate position of RFI in the preliminary extraction results is located by the fuzzy C-means clustering; then, we separate the RFI and the remaining useful signals again and reconstruct the useful signals to complete the mitigation work. The proposed method can further protect useful signals while effectively removing interference through the secondary separation of RFI. Experimental results based on simulated and measured data verify the performance and potential of the proposed method.

show abstract

Section: Semi-parametric Methodsmentioning

confidence: 99%

An Improved RFI Mitigation Approach for SAR Based on Low-Rank Sparse Decomposition: From the Perspective of Useful Signal Protection

Zhang

Lin

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…According to the assumption of the joint low rank and sparse model, various robust principal component analysis-based WBI mitigation and target echo reconstruction methods have been proposed [10][11][12][13]. Huang proposed an NBI mitigation method based on matrixfactorization decomposition, and it is also applied in WBI mitigation in SAR data and image domain [14,15]. Meanwhile, Huang proposed a proficient method for reducing WBI through an alternating projections approach, which exhibits excellent performance and rapid convergence in the two-dimensional frequency domain [16].…”

Section: Related Workmentioning

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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…In contrast to image detection and foreground-background separation, which involve processing real data, SAR echo data is represented as a two-dimensional complex matrix. The soft threshold algorithm is preferred over the hard threshold algorithm due to its ability to mitigate additional reconstruction errors associated with complex data processing and expedite algorithm convergence [35]. Consequently, the soft threshold algorithm is employed for solving (9) to update the sparse matrix, where ζ denotes the initial threshold and γ represents the threshold attenuation parameter.…”

Section: B Update Sparse Matrix Smentioning

confidence: 99%

A SAR Multiple RFI Suppression Method via Frobenius Norm and Iterative Matrix Decomposition

Guo,

Tian,

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

The scarcity and sharing nature of the electromagnetic spectrum present a significant challenge to the stable operation of Synthetic Aperture Radar(SAR) systems, as they are susceptible to interference from other devices operating in the same frequency band, known as Radio-Frequency Interference(RFI). We propose an effective semi-parametric method for suppressing Multiple RFI(MRFI), named the iterative matrix decomposition algorithm based on the Frobenius norm(FIMD), we employ CUR decomposition and a soft threshold algorithm to update low-rank and sparse matrices within an alternate projection framework. It is observed that there exist distinct distribution characteristics between interference points and strong scattering points in the echo domain. We propose a novel and effective signal protection method, which effectively mitigates the risk of strong scattering points being misidentified as interference signals and subsequently eliminated. Additionally, we utilize random singular value decomposition instead of traditional singular value decomposition to enhance convergence speed of. Simulation results demonstrate that our proposed method exhibits superior suppression capability and robustness under varyingto-interference ratio conditions and it can be applied to L0-raw products and L1-SLC products.

show abstract

Radio Frequency Interference Mitigation for Synthetic Aperture Radar Based on the Time-Frequency Constraint Joint Low-Rank and Sparsity Properties

Cited by 10 publications

References 35 publications

An Improved RFI Mitigation Approach for SAR Based on Low-Rank Sparse Decomposition: From the Perspective of Useful Signal Protection

An Improved RFI Mitigation Approach for SAR Based on Low-Rank Sparse Decomposition: From the Perspective of Useful Signal Protection

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

A SAR Multiple RFI Suppression Method via Frobenius Norm and Iterative Matrix Decomposition

Contact Info

Product

Resources

About