Efficient and Robust RFI Extraction Via Sparse Recovery

This paper presents a simple adaptive framework for robust separation and extraction of multiple sources of radio-frequency interference (RFI) from raw ultra-wideband (UWB) radar signals in challenging bandwidth management environments. RFI sources pose critical challenges for UWB systems since 1) RFI often occupies a wide range of the radar's operating frequency spectrum; 2) RFI might have significant power; and 3) RFI signals are difficult to predict and model due to the nonstationary nature as well as the complexity of various communication devices. Our proposed framework involves an initial RFI estimation step that operates directly on already contaminated radar signals to identify RFI-dominant frequency sub-bands. This vital prior information is then taken into account to construct an adaptive RFI dictionary with various sinusoidal patterns covering the aforementioned RFI-contaminated frequency spectrum. Finally, we employ a sparsity-driven optimization strategy to separate and then extract RFI from the received radar signals. Our method can be implemented as a denoising preprocessing stage for raw radar signals prior to image formation and other follow-up tasks such as target detection and classification. Recovery results from extensive simulated data sets as well as real-world signals collected by the U.S. Army Research Laboratory (ARL) UWB synthetic aperture radar (SAR) systems illustrate the robustness and effectiveness of our proposed framework.