Estimation and extraction of radio-frequency interference from ultra-wideband radar signals

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 (i) RFI often occupies a wide range of the radar´s operating frequency spectrum; (ii) RFI might have significant power; and (iii) RFI signals are difficult to predict and model due to their non-stationary nature as well as the complexity of various communication devices. Our proposed framework involves a standard RFI detection step that operates directly on previously-collected contaminated radar signals to identify RFI-dominant frequency sub-bands. This vital information is then applied to construct an RFI dictionary with various sinusoidal patterns spanning these RFI bands. We then employ a sparsity-driven optimization to estimate and then extract RFI from the received radar signals. Our method can be considered as a de-noising preprocessing stage for raw radar signals prior to image formation and other follow-up tasks. Recovery results from extensive simulated as well as real-world UWB synthetic aperture radar (SAR) data sets illustrate the robustness and effectiveness of our framework.