PROJECT TITLE:

Efficient and Robust RFI Extraction Via Sparse Recovery - 2016

ABSTRACT:

This paper presents a simple adaptive framework for sturdy 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 create essential challenges for UWB systems since one) RFI often occupies a wide range of the radar's operating frequency spectrum; 2) RFI might have vital power; and 3) RFI signals are tough to predict and model due to the nonstationary nature also because the complexity of varied 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 previous information is then taken under consideration to construct an adaptive RFI dictionary with numerous sinusoidal patterns covering the aforementioned RFI-contaminated frequency spectrum. Finally, we tend to use a sparsity-driven optimization strategy to separate and then extract RFI from the received radar signals. Our methodology will be implemented as a denoising preprocessing stage for raw radar signals previous to image formation and different follow-up tasks like target detection and classification. Recovery results from intensive simulated knowledge sets also real-world signals collected by the U.S. Army Research Laboratory (ARL) UWB artificial aperture radar (SAR) systems illustrate the robustness and effectiveness of our proposed framework.