Robust and adaptive extraction of RFI signals from ultra-wideband radar data

Abstract

In this paper, we propose a novel, robust, and adaptive technique for the extraction of radio frequency interference (RFI) signals from ultra-wideband (UWB) radar data via sparse recovery. Unlike notch-filtering techniques that have been widely employed in the past, our proposed technique directly estimates and suppresses RFI signals from the UWB radar signal directly in time domain. Therefore, it does not suffer from several detrimental side effects such as high-sidelobe distortion and target-amplitude reduction as often observed in notch-filtering approaches. In addition, the technique is completely adaptive with highly time-varying environments and does not assume any knowledge (from frequency band to modulation scheme) of the RFI sources. The proposed technique is based on a sparse-recovery approach that simultaneously solves for (i) the UWB radar signal embedded in RFI noise with large amplitudes and (ii) RFI signals. Using both simulated and real-world data measured by the U.S. Army Research Laboratory (ARL) UWB synthetic aperture radar (SAR), we show that our proposed RFI extraction technique successfully recovers the UWB radar signal embedded in large-amplitude RFI signals. An average of 12 dB of RFI suppression is consistently realized in the real radar data experiments.