Joint sparse and low-rank model for radio-frequency interference suppression in ultra-wideband radar applications

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 in challenging bandwidth management environments. RFI sources poses 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 as well as the complexity of various communication devices. Existing techniques for RFI suppression either employ filtering (notching) which introduces other harmful side-effects such as side-lobe distortion and target-amplitude reduction or RFI modeling/estimation/tracking which requires complicated narrow-band modulation models or even direct RFI sniffing. We explore in this paper a joint sparse and low-rank model for the separation and then suppression of RFI signals from UWB radar data via modeling RFI as low-rank components in a joint optimization framework. The proposed framework is completely adaptive with highly time-varying environments, does not require any prior knowledge of the RFI sources (other than the low-rank assumption), and is capable of processing already-contaminated radar directly. Both simulated data and real-world data measured by the U.S. Army Research Laboratory (ARL) UWB synthetic aperture radar (SAR) confirm that our RFI suppression technique successfully recovers UWB radar signals corrupted by high-powered RFI signals.