Radio-frequency interference separation and suppression from ultrawideband radar data via low-rank modeling

Radio-frequency interference (RFI) is the most common, and also the most challenging type of interference or noise source that has a direct impact on the performance of ultrawideband radar systems in various practical application settings. 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. In this paper, we propose a robust and adaptive technique for the separation and then suppression of RFI signals from ultra-wideband (UWB) radar data via modeling RFI as low-rank components in a joint optimization framework. More specifically, we advocate a joint sparse-and-low-rank recovery approach that simultaneously solves for (i) UWB radar signals as sparse representations with respect to a dictionary containing transmitted waveforms; and (ii) RFI signals as a low-rank structure. The proposed technique is completely adaptive with highly time-varying environments, and does not require any prior knowledge of the RFI sources (other than the low-rank assumption). Both simulated data and real-world data measured by the U.S. Army Research Laboratory (ARL) Ultra-Wideband (UWB) synthetic aperture radar (SAR) confirm that the proposed RFI separation/suppression technique successfully recovers UWB radar signals embedded in large-amplitude RFI signals.