A Joint Framework of Wavelet Filtering and Fast GSVT-LRSD Algorithm for SAR Narrowband Pulsed RFI Suppression

As the electromagnetic spectrum becomes increasingly crowded in recent years, synthetic aperture radar (SAR) is confronted with an escalating amount of radio-frequency interference (RFI). In civilian SAR satellite data, narrowband pulsed RFI (PRFI) is a prevalent interference type that significantly degrades the interpretability of SAR images. Among most approaches for suppressing PRFI, notch filtering methods face significant challenges in threshold selection of signal intensity. Conversely, low-rank and sparse decomposition (LRSD) algorithms, though free of threshold selection, often struggle to satisfy the required low-rank conditions. These limitations underscore the necessity of developing more robust interference suppression methods. In this article, we propose a joint framework of wavelet filtering and fast generalized singular value thresholding-based LRSD (FGSVT-LRSD) method to suppress narrowband PRFI in range–frequency and azimuth–time domain of SAR single-look complex (SLC) data. First, a wavelet domain notch filtering (WNF) method is employed to extract the strong spectral components that are primarily composed of PRFI in the 2-D range spectrum of SAR SLC data, while simultaneously protecting the spectrum of low-energy useful signals. Then, the FGSVT-LRSD method is applied to the extracted strong spectral components to efficiently separate the PRFI from the useful signals. By strategically integrating these two approaches, the proposed framework simultaneously exploits the high-intensity and low-rank properties of PRFI for more precise separation, significantly reduces the sensitivity of threshold selection in the WNF process and facilitates the low-rank conditions required by the FGSVT-LRSD method. Finally, the separated PRFI spectrum is subtracted from the original 2-D range spectrum, resulting in the RFI-suppressed SAR image. Experimental results based on both simulated and measured spaceborne SAR data will be presented to demonstrate that, compared with existing methods, the proposed approach exhibits superior PRFI suppression capabilities and effectively preserves useful signals.