Pulse RFI Mitigation for SAR Data Based on Reduced Rank Approximate

Radio frequency interference (RFI) caused by spectrum allocation issues can adversely affect the normal operation of synthetic aperture radar (SAR). Compared to traditional RFI, pulsed RFI (PRFI) typically has higher power and wider bandwidth, making the resulting artifacts more likely to obscure the targets of interest. The notch filtering methods are robust approaches to mitigating PRFI. However, the proportion of PRFI in SAR data increases as the electromagnetic environment gradually deteriorates, and these methods inevitably lose too much useful signal. Moreover, traditional semi-parametric methods based on nuclear norms (NNs) over-penalize singular values, leading to the inaccurate estimation of low-rank components. To overcome the aforementioned problems, this article proposes a semi-parametric method. Our method is based on the Hankel structure and truncated nuclear norm (TNN) constrained low-rank estimation model to mitigate strong PRFI while preserving valuable signals. First, the SAR echoes containing PRFI are detected using a relative energy ratio algorithm. Then, the low-rank properties of PRFI are linearly expanded and enhanced through the Hankel structure. Finally, TNN regularization is employed to obtain more accurate low-rank approximations than traditional NN, thereby separating PRFI. Experiments based on simulated and measured data from ESA Sentinel-1A validated the usefulness and preeminence of our method.