Due to the wide bandwidth and large observation area, spaceborne synthetic aperture radar (SAR) is easily interfered by other electromagnetic signals, namely radio frequency interference (RFI), which can severely degrade SAR image quality and submerge useful information. Classic parametric and non-parametric methods are used to suppress RFI as much as possible without considering the useful information. To protect the real reflected signals, semi-parametric methods, based on low-rank and sparse recovery, are proposed to mitigate RFI, but they suffer from the singular-value over-shrinking problem when RFI is not strictly low-rank, resulting in interference residues in the recovered scene. Hence, in this paper, a robust interference self-cancellation (RISC) method is proposed to protect raw ground scenes from polluted data with better extraction accuracy of RFI. The proposed model can adaptively fit in different scenes and backgrounds by using adjacent homologous interference (HI) subregions instead of the low-rank constraints, thus better protecting SAR scenes and enhancing its robustness. Based on the alternating direction method of multipliers (ADMM), we design two different solvers for the proposed optimization model, and both are tested on four different scenes of Sentinel-1 measured data. All experiments demonstrate that the proposed method has excellent performance in RFI mitigation and SAR image recovery.

As a wideband radar system, spaceborne synthetic aperture radar (SAR) has been widely applied in multiple applications, such as maritime surveillance and terrain observation. However, with the increase of electromagnetic devices, spaceborne SAR suffers from radio frequency interference (RFI) frequently. Many previous methods have been effective in interference mitigation, among which semiparametric methods demonstrate excellent performance and high efficiency. However, as a classic low-rank recovery method, robust principal component analysis (RPCA) usually suffers from the over-penalization problem of large singular values. Although some useful schemes were proposed to address this issue, their performance may still degrade if the low-rank characteristics of interference are not prominent. To overcome these obstacles, we first investigate the characteristics and distributions of different SAR signals and leverage kurtosis to differentiate interference and real echoes. Herein, interference tends to have a low kurtosis while the real echoes tend to have a high kurtosis. Then, we improve the low-rank recovery model with kurtosis and propose the kurtosis-based reweighted nuclear norm (KRNN) model to precisely extract interference components. Then, we derive the closed-form solution of the KRNN model via the alternating direction method of multipliers (ADMM) framework. Through the proposed KRNN method, we can effectively mitigate interference, preserve real echoes, and solve the problems of over-penalization and nonideal low-rank property. Finally, we conduct numerical experiments using the measured Sentinel-1 and LT-1 data to demonstrate the effectiveness and robustness of our proposed method.

As an advanced remote sensing technology, synthetic aperture radar (SAR) generates high-resolution images by transmitting continuous electromagnetic waves toward the target area. SAR has played a pivotal role in both contemporary research and practical applications. This underscores the importance of maintaining imaging integrity. However, the performance of SAR systems is severely affected by the increasingly prevalent radio frequency interference (RFI). RFI not only degrades the quality of SAR images but also hinders the accurate interpretation of SAR data. The rapid development and widespread use of modern electromagnetic devices have led to a diversification of interference types, resulting in complex mixed-mode interference. Traditional interference mitigation techniques struggle to effectively alleviate these issues. Moreover, varying terrains add significant difficulty to mitigating interferences, often resulting in residual interference in processed images and the loss of substantial scene information. To tackle these challenges, this article proposes a novel interference mitigation method called the group-parametric method. Unlike previous semiparametric methods, the group-parametric method refines both the interference and target models and achieves more effective interference mitigation and scene preservation by applying distinct regularizations to the refined models. Based on the new model, we have designed a structured trifactorization (STF) algorithm across frequency and time domains, which achieves data recovery through regularizations of low-rank and sparsity applied to the interference. Experimental verification with Level-1 data from LuTan-1 (LT-1) and Sentinel-1 confirms the effectiveness and superiority of our proposed model and method.

Synthetic aperture radar (SAR) is a kind of active microwave remote sensing imaging radar, which can obtain high-resolution two-dimensional SAR images. As a multi-parameter, multi-channel SAR, polarimetric SAR (PolSAR) provides rich scattering information for topographic mapping, ocean exploration, polar observation, target identification, and many other fields. Compared to single-polarization SAR, multi-polarization SAR greatly improves the potential information of the data by extending the one-dimensional information. However, the above tasks cannot be carried out without clean SAR echo signal. The radio frequency interference (RFI) signals, seriously affect the subsequent tasks of PolSAR, and there is a great deal of potential information between polarized data. Therefore, this paper proposes a framework for combining multiple polarization data to improve low-rank based methods’ performance. Based on the proposed framework, one experiment is conducted on real PolSAR data, the experiment uses the PCA method to verify the applicability of the proposed framework in interference suppression. At last, the result verifies the framework achieves better suppression of low-rank based method.

Synthetic Aperture Radar (SAR) is a type of active microwave remote sensing imaging radar that can generate two-dimensional high-resolution images. Its ability to operate in all weather conditions and at all times has led to its widespread use. As a multi-parameter and multi-channel extension of SAR, polarimetric SAR (PolSAR) provides a wealth of scattering information for various applications, including topographic mapping, ocean exploration, polar observation, and target identification. Compared with single-polarization SAR, multi-polarization SAR enhances the information potential of the data by expanding its one-dimensional information, however, this potential cannot be fully realized without a clean SAR echo signal. The electromagnetic environment is becoming increasingly congested with radio frequency interference (RFI) signals, presenting a significant challenge for the subsequent tasks of PolSAR. Although there have been many related studies based on polarization information to carry out the aforementioned applications, there is a lack of research on the joint suppression of interference by using multi-polarization information, and single-polarization data alone is insufficient in effectively mitigating interference. To address these challenges, this paper presents a framework combining multi-polarization data to improve performance of low-rank based methods. Based on the proposed framework, experiments are conducted on real PolSAR data to assess the feasibility of the proposed framework in interference suppression. The results demonstrate that the framework significantly enhances the suppression performance of various low-rank based methods with clearer scene details being recovered.

Synthetic aperture radar (SAR) is a kind of active imaging radar, which can obtain high-resolution wide-swath SAR images, especially for spaceborne SAR systems. In practical electromagnetic environment, due to the overlap of same frequency bands, spaceborne SAR is extremely vulnerable to interferences from other electromagnetic systems, called radio frequency interference (RFI) to SAR systems. RFI seriously reduces the imaging quality of the SAR system and causes resolution reduction and scene occluded. To mitigate RFI in SAR systems, researchers have proposed many methods, in which semi-parametric methods, such as robust principal component analysis (RPCA)-based methods, played important roles in strong RFI mitigation in recent years. However, it is observed that they may be hard to recover the true scene well under extremely strong RFIs since the strong scatterers are also mixed in the extracted low-rank interferences. Therefore, in this paper, we propose a novel adaptive method, which combines the advantages of both semi-parametric method and frequency domain notched filter (FNF) method, called adaptive notch semi-parametric (ANSP) method, where the FNF method, as a non-parametric method, can retain more true scenes when mitigating interferences. As a result, the proposed method can not only effectively deal with strong RFIs but also protect the strong scatterers better with an adaptive threshold. This method can recover the true scene under extremely strong RFI and be applied to both Level-0 and Level-1 SAR data. Finally, we conduct experiments on several real SAR data and demonstrate the effectiveness of the proposed method.