RFI-radar signal separation via simultaneous low-rank and sparse recovery

Abstract

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 via simultaneous low-rank and sparse recovery 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 the non-stationary nature and the complexity of various communication devices. Our proposed framework advocates a joint sparse-and-low-rank recovery approach in the spirit of robust principal component analysis (RPCA) that simultaneously solves for RFI signals as low-rank structures and UWB synthetic aperture radar (SAR) signals as sparse impulsive outliers. Our technique is completely adaptive in highly time-varying environments and does not require any prior knowledge of the RFI sources (other than the low-rank assumption). Our method can be implemented as a denoising pre-processing 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 SAR systems illustrate the robustness and effectiveness of our framework.