RFI detection and mitigation using independent component analysis as a pre-processor

Radio-frequency interference (RFI) has negatively impacted scientific measurements of passive remote sensing satellites. This has been observed in the L-band radiometers Microwave Imaging Radiometer with Aperture Synthesis (MIRAS) for the Soil Moisture and Ocean Salinity (SMOS) mission, Aquarius and more recently, Soil Moisture Active Passive (SMAP). RFI has also been observed at higher frequencies such as K band. Improvements in technology have allowed wider bandwidth digital back ends for passive microwave radiometry. A radio frequency interference detector based on complex signal kurtosis was developed to help identify corrupted measurements. This work explores the use of Independent Component Analysis (ICA) as a blind source separation (BSS) technique to pre-process radiometric signals for use with the previously developed real and complex signal kurtosis detectors.