Digital Beamforming for Radio Frequency Interference Suppression in Synthetic Aperture Radar

Abstract

The contamination of Synthetic Aperture Radar (SAR) data by Radio Frequency Interference (RFI) has become an increasing problem. Methods to mitigate RFI are therefore critical for the successful operation of a SAR mission. In a conventional SAR, RFI correction relies on notching methods that also remove parts of the SAR signal. Otherwise, a-priori knowledge about the interfering signal characteristics is required and the filtering performance strongly depends on the observed scene and interference. New SAR instruments employ a multi-channel architecture capable of Digital Beamforming (DBF). These systems can spatially filter the received signals and give rise to new RFI mitigation techniques. The antenna pattern can be adaptively set after acquisition to null the angular direction of the RFI signal. Even more, DBF gives the opportunity to individually measure RFI and SAR signals arriving from different directions. This allows gathering information about the interfering signal characteristics from the data. This work proposes novel DBF-based RFI mitigation techniques that utilize the spatial distribution of the SAR signal in the range-Doppler domain. The spatial distribution is variable in time and yet predictable because the arrival time of the radar return is determined by the imaging geometry. The proposed new methods can be divided into two categories. The first new approach adaptively nulls the antenna pattern throughout the SAR data to suppress the RFI signal. An implemented simulator is used to verify the performance and results of experimental airborne data is presented. The second new approach steers simultaneous digital beams into the direction of the interferers to measure and subtract them from the data. The measured interferer signal characteristics can then be used to estimate the RFI signal for time instances when a spatial filtering is not possible. Simulation results for this approach are presented.