Development of Radio Frequency Interference Detection Algorithm for PassiveMicrowave Remote Sensing

Radio Frequency Interference;Microwave Radiometry;RFI;Remote Sensing;Atmospheric;Oceanic and Space Sciences;Science;Atmospheric and Space Sciences

Misra, SidharthSkou, Niels ;

University of Michigan

关键词: Radio Frequency Interference;    Microwave Radiometry;    RFI;    Remote Sensing;    Atmospheric;    Oceanic and Space Sciences;    Science;    Atmospheric and Space Sciences;   

Others  :  https://deepblue.lib.umich.edu/bitstream/handle/2027.42/86308/samisra_1.pdf?sequence=1&isAllowed=y

瑞士|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

Radio Frequency Interference (RFI) signals are man-made sources that are increasingly plaguing passive microwave remote sensing measurements.RFI is of insidious nature, with some signals low power enough to go undetected but large enough to impact science measurements and their results.With the launch of the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite in November 2009 and the upcoming launches of the new NASA sea-surface salinity measuring Aquarius mission in June 2011 and soil-moisture measuring Soil Moisture Active Passive (SMAP) mission around 2015, active steps are being taken to detect and mitigate RFI at L-band.An RFI detection algorithm was designed for the Aquarius mission.The algorithm performance was analyzed using kurtosis based RFI ground-truth.The algorithm has been developed with several adjustable location dependant parameters to control the detection statistics (false-alarm rate and probability of detection).The kurtosis statistical detection algorithm has been compared with the Aquarius pulse detection method.The comparative study determines the feasibility of the kurtosis detector for the SMAP radiometer, as a primary RFI detection algorithm in terms of detectability and data bandwidth.The kurtosis algorithm has superior detection capabilities for low duty-cycle radar like pulses, which are more prevalent according to analysis of field campaign data.Most RFI algorithms developed have generally been optimized for performance with individual pulsed-sinusoidal RFI sources.A new RFI detection model is developed that takes into account multiple RFI sources within an antenna footprint.The performance of the kurtosis detection algorithm under such central-limit conditions is evaluated.The SMOS mission has a unique hardware system, and conventional RFI detection techniques cannot be applied.Instead, an RFI detection algorithm for SMOS is developed and applied in the angular domain.This algorithm compares brightness temperature values at various incidence angles for a particular grid location.This algorithm is compared and contrasted with other algorithms present in the visibility domain of SMOS, as well as the spatial domain.Initial results indicate that the SMOS RFI detection algorithm in the angular domain has a higher sensitivity and lower false-alarm rate than algorithms developed in the other two domains.