A RFI Mitigation Approach for Spaceborne SAR Using Homologous Interference Knowledge at Coastal Regions

As a form of direct signal, radio frequency interference (RFI) received by spaceborne synthetic aperture radar (SAR) systems significantly submerges the useful echoes scattered from the ground scene. Therefore, one of the effective models is to represent RFI as a low-rank component, and the method with a low-rank and sparse framework can be used to suppress the RFI and extract the useful signals. These methods involve extracting the underlying RFI component directly from the polluted SAR signal matrix by minimizing its nuclear norm. However, in recent years, many studies have shown that it is difficult for the nuclear norm minimization (NNM) model to obtain an accurate singular value estimation of the RFI component from polluted observed signals. Observed from the Sentinel-1 RFI distribution published by European Space Agency (ESA), a large amount of RFI occur at coastal regions in the world. In this article, an interference self-cancellation (ISC) method using the knowledge of homologous interference (HI) at coastal regions is proposed to mitigate the RFI in the polluted SAR data. Herein, HI is defined as the RFI with similar artifacts in the same SAR image. We extract the singular value estimation of the reference HI area and add it to the low-rank model as a template, relieving overshrinking problem of the classic NNM algorithms. In addition, the proposed method can be considered as an extended framework of the previous low-rank methods. When the ideal HI cannot be extracted, the proposed method can be degraded into a previous NNM method, such as robust principal component analysis (RPCA) method or reweighted nuclear norm (RNN) method. Subsequently, we test the performance of the proposed algorithm in multiple different scenes obtained from measured Sentinel-1 data. All experiments verify that the proposed ISC method can remove RFI and keep more details of ground scenes compared to other state-of-the-art RFI mitigation methods.