An RFI Mitigation Method on Spaceborne SAR via Kurtosis-Based Reweighted Nuclear Norm

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.