Evaluating Radio Frequency Interference Detection Algorithms for SMAP (Soil Moisture Active Passive)
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Date
2013-12
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The Ohio State University
Abstract
SMAP (Soil Moisture Active Passive) is a mission to be launched by NASA to measure soil
moisture of the Earth’s land surface. The SMAP radiometer operates in the L-band protected
spectrum (1400-1427 MHz) that is known to be vulnerable to radio frequency interference (RFI).
Radiometric observations show substantial evidence of out-of-band emissions from neighboring
transmitters and possibly illegally operating emitters. SMAP faces large levels of RFI and also
significant amounts of low-level RFI equivalent to 0.1 K to 10 K of brightness temperature. Such
low-level interference would be enough to jeopardize mission success without an aggressive
mitigation solution.
A decision has been made to employ an advanced digital microwave radiometer, the first of its
kind for spaceflight, for use on SMAP. The mission takes a multi-domain approach to RFI
mitigation utilizing an innovative on-board digital detector backend with DSP algorithms to
detect and filter out harmful interference. Four different baseline RFI detectors are run on the
ground and their outputs combined for a maximum probability of detection to remove RFI within
a footprint. The SMAP radiometer outputs the first four raw moments of the receiver system
noise voltage in 16 frequency channels for measurement of noise temperature and kurtosis as
well as complex cross-correlation products for measuring the third and fourth Stokes parameters.
Evaluating each of the four individual RFI detection algorithms is essential to ensure the highest
efficiency produced by the maximum probability of detection. Receiver operating characteristic
(ROC) curves are generated for each of the different detectors to evaluate performance. ROC
curves plot the probability of detection versus false alarm rate. The optimum case would
correspond to the highest probability of detection (PD) and lowest false alarm rate (FAR). A
given threshold for the RFI algorithms would produce a corresponding (PD, FAR). The rest of
the line curve is graphed by varying threshold from a minimal value to a maximal value. The
ROC curves are performed on all different RFI algorithm detectors which include time-domain,
cross-frequency, kurtosis, and polarization detectors. Each detector operates differently and
behaves differently under different injected RFI. Different injected RFI include pulsed and
sinusoidal at different frequencies, amplitudes, and power.
The focus of the study is to optimize each of the given RFI detectors given any RFI signal. For
example, since the cross-frequency algorithm uses only frequency resolution and no time
resolution, its performance should be best for RFI that is localized in frequency. Since
continuous wave (CW) RFI are localized in frequency by definition, as expected, the cross-
frequency detector performed very well against CW RFI relative to other detectors. The RFI
detection performance that is ultimately achieved will be a function of the threshold (that returns
the highest PD versus lowest FAR), the nature of the RFI encountered, and radiometer system
parameters such as the number of frequency channels and the integration period.
Description
Keywords
Radio Frequency Interference, Microwave Radiometery, Detection Algorithms, Receiver Operating Characteristic Curves (ROC), Active and Passive Sensing