A comprehensive performance comparison of RFI mitigation techniques for UWB radar signals

This paper presents a comprehensive benchmark comparison in objective as well as subjective performances of radio-frequency interference (RFI) suppression/extraction techniques for ultra-wideband (UWB) signals in synthetic aperture radar (SAR) imaging applications. In this study, we employ two sets of UWB SAR signals: one simulated from a step-frequency radar setup, whereas the other is collected on the testing field in a real-world setup from the U.S. Army Research Laboratory. Similarly, our RFI experiments involve two RFI data sets: one is simulated from a collection of randomly generated frequency bands and the other is the RFI data collected in a real-world environment with the radar receiving antenna pointing toward Washington DC. These SAR and RFI data sets represent four diverse experimental setups where we can carefully benchmark the denoising performance of several popular RFI-mitigation techniques in the current literature based on notch-filtering, principal component analysis (PCA), model-based sparse recovery, and simultaneous low-rank and sparse recovery or robust PCA (RPCA). We validate that RPCA and model-based sparse recovery consistently yields the best overall RFI separation performance on a wide range of settings in all data sets.