Methods of Developing
Radio Frequency Interference
Mitigation for Microwave Radiometry
Roger De Roo
Outline
• Monitoring the soil moisture with microwave
radiometry
– Motivation
– Physics of passive remote sensing
• Radio Frequency Interference: a major problem
– Summary of RFI detection approaches
– Methodologies of the kurtosis algorithm development
– Conclusions
Soil Moisture: Active and Passive (SMAP)
NASA
environmental
satellite
Currently in
planning stages
Launch 2010-2013
Soil Moisture: who cares?
• Soil Moisture
regulates plant
transpiration
• Transpiration
determines
humidity
• Humidity gives
rise to clouds
• No widespread
measurements
of soil moisture
currently
What’s so great about Microwave Remote Sensing?
Long wavelengths (3mm to 30cm) don’t scatter off of objects the size of
cloud droplets -- microwaves see through clouds
Radar
•Very high spatial resolution
•Power hungry: expensive
•Sensitive to geometry of water:
eg. Movement of trees causes
big signal changes
Tx
Rx
Radiometry
•Poor spatial resolution
•Low power requirements
•Insensitive to geometry of water
Rx
Planck Blackbody Radiation
3000K
red hot
6000K
white hot
the Sun
300K
room temp
30K
3K
outer space
wavelength
1 GHz
0.3 m
1 THz
0.3 mm
1 PHz
0.3 um
frequency
Microwave Brightness and Moisture
• Water molecules have large electric dipole, unlike rest of nature
H
- O
+
H
Liquid water molecules will orient itself with passing
electromagnetic waves,slowing the wave down
The molecule can keep up with the wave until 9 GHz
(index of refraction: n = 9 at 1GHz, but n = 2 at 100 GHz)
• An interface w/ high contrast of index of refraction leads to reflection
• Dry soils appear warm, while wet soils appear cold, at the same temp.
Space 2.7K
Space 2.7K
Sensor
Sensor
Dry Soil
~300K
Wet Soil
~300K
Example Brightness Image from Space
NASDA
Sensitivity D TB / D VSM (K/%)
Sensitivity of Radiobrightness to Soil
Moisture Under a Vegetation Canopy
4.0
BARE
0
3.0
1
VEGETATION
WATER CONTENT
(kg/m2)
2
2.0
4
1.0
0.0
0
5
SSM/I
AMSR
19 GHz 6.9 GHz
10
15
Wavelength (cm)
20
25
SMAP
1.4 GHz
Courtesy of P. O’Neill
University of Michigan Radiometers
L-band
1.4 GHz
l = 21 cm
C-band
6.7 GHz
l = 4.5 cm
satellites:
none yet!
SMOS ‘08
Aquarius ‘10
SMAP ‘14
Antenna size
is proportional
to wavelength
satellites:
AMSR-E ‘02
19 GHz
37 GHz
l = 1.6 cm
l = 0.8 cm
satellites:
SSM/I etc. ‘87 to present
Diurnal Brightness Measurements
Brightness of Tundra and Shrubs
Trouble with the 1.4GHz Radiometer
Both of these ranges
appear plausible
Potentially Interfering Radars:
Cobra Dane
Peak Transmit Power 16.8 MW
Transmit Frequency 1.215-1.375GHz
Raytheon
Surrounded by Interfering Radars?
FPS-124
FPS-108
Cobra Dane
Observation site:
Toolik Lake
FPS-117
ITT, ‘05
AMSR-E Interference at 6.9GHz
If it is not purple, we cannot use the data from that location
If it is purple, the data from that location might be OK…or not
Li et al., ’04
Approaches to Detecting RFI
1.
Time domain – look for pulses
2.
Frequency domain – look for carrier frequencies
3.
Amplitude domain – look for non-thermal distribution
Thermal waveform
Gaussian pdf
Sinusoidal waveform
Non-Gaussian pdf
Digital Radiometry
Digital radiometers use fast analog-to-digital converters
to measure the voltage waveform
Power is determined by finding
the variance (2nd moment)
of the quantized data
Processing capability allows
for implementation of one or
more RFI mitigation strategies
Methods
•
•
•
•
•
Lit search!
Theory development (analysis – in the math sense of the word)
Simulation
Highly controlled experiments in the laboratory
Less controlled experiments in the field
Literature Search
• What has already been done on this problem, or related problems?
• For RFI mitigation, nothing in the amplitude domain. Some in timedomain and some in frequency domain.
• However, testing for normality of a distribution does have a rich
literature. Lotsa ways to do it, and it is known how well they work.
Is it Normal? The kurtosis
Statistical moments:

n   v n p(v)dv

•
•
•
•
•
0th… event count
1st… Mean
2nd …Variance
3rd … Skew
4th … Kurtosis
Skew
• Measures how
asymmetric a
distribution is
• Normal distribution
has zero skew
• So does RFI 
Kurtosis
• Kurtosis measure
“peakedness” of a
distribution
• Normal distribution
has kurtosis = 3
• RFI can have any
kurtosis
Theory Development
• Paper and pencil derivations of what to expect
• Requires assumptions:
• Results are general – if the assumptions hold true
Geothermal
Signal
Atmosphere
Radio
Frequency
Interference
Atmosphere
Antenna
Receiver
Data
Processor
Theory Development: some assumptions
All curves have the
same variance:
A radiometer will
report all of
these signals as
the same
brightness
Pulsed
sinusoid to
noise ratio:
S = dA2/2σ2
Simulation
•
•
•
•
Mimic the expected process in a computer
Use to verify/ validate the analysis
Not general: Can only conclude about the cases simulated.
Can also be used to extend the analysis to cases that violate the
assumptions
Simulation
• One example:
Spread of
kurtosis due
to finite no.
of samples
Curves are a
prediction
Dots are
calculated by
computer
program
Laboratory Experiments
• Check
assumptions about
radiometer
operation
• RFI prescribed to
conform to theory
assumptions
DetMit Rcvr
CNCS RF head
Arbitrary
Waveform
Generator
ADD
ADD Data Acq
CNCS Control
Variable
Attenuator
Laboratory Experiment Results
• Curves are theory
• Marks are data
• They match!!!
Field Experiments
• Check assumptions about RFI: very realistic environment
• No control of when RFI comes, or what kind it is, or how strong.
TSYS (counts2)
RFI flags
sky
absorber
seconds
sky
Field Experiments
• A lot of fun to do!
• Takes lots of people ($$$) to do.
Field Campaign Results
Conclusions
• Different research methods have different strengths and weaknesses
• For RFI research, the methods can be considered to lie on a spectrum
Theory Simulation Laboratory Experiment Field Experiment
Most general
Very Specific
Many assumptions
Very Realistic
Relatively cheap
Quite Expensive
• Combinations of methods draws on strengths of each
Thank You!
Backup Slides
Tanana River Breakup at Nenana
Spring Breakup
145
140
Day of Year
135
130
125
120
115
110
Guess the moment of breakup!
Tickets cost $2.50 each
Typical Jackpot: $300,000+
www.nenanaakiceclassic.com
105
1900
1920
1940
1960
Year
1980
2000
2020
Observed Global Temperature Trends
IPCC ‘01
Projected Global Temperature Trends
2071-2100 temperatures relative to 1961-1990.
Special Report on Emissions Scenarios Storyline B2 (middle of the road warming).
IPCC ‘01
Atmospheric stock is about 750PgC
m
pe
ica
lF
or
es
ra
te
ts
F
Bo
or
r
ea est
Tr
s
l
Te opic Fo
re
al
m
pe
Sa sts
ra
va
te
nn
G
as
ra
ss
la
nd
s
D
es
er
ts
Tu
nd
C
ra
ro
pl
an
W ds
et
la
nd
s
Te
Tr
op
Total Carbon (PgC)
Carbon Stocks by Biome
500
450
400
350
300
250
200
150
100
50
0
plants
soils
IPCC ‘01
Permafrost extent
Global Terrestrial Network for Permafrost
20m Borehole Temperature Trends in AK
Hinzman et al 2005
Permafrost structure
NSIDC
Active Layer Depth Trends
Active Layer Depth (cm)
80
70
60
50
40
30
20
10
0
1990
1995
2000
Year
2005
Barrow
Barrow, (CRREL)
Atkasuk
West Dock
West Dock
Deadhorse
Betty Pingo
Franklin Bluff
Happy Valley
Happy Valley
Imnavait Creek
Toolik
Toolik LTER
Galbraith Lake
Circumpolar Active Layer Monitoring Network
The Tundra Landscape
Strategy for Estimating Temperature and
Moisture Profiles
Atmospheric Model
Satellite L-band Radiometer
Tb(observed)
Weather & downwelling radiance
SVAT Model
Assimilate Tb(observed) - Tb(model)
Temperature & Moisture Profiles
Tb (model)
Radiobrightness Model
Temperature (K)
Temperature (K)
Temperature (K)
3 20
Calibrated LSP/R model of Prairie Grassland
3 10
3 00
2 90
2 80
1 82
3 20
3 10
SGP'97 (TIR)
LSP/R (Canopy)
LSP-SGP = -0.28 K
Variance = 3.28 K
1 84
1 86
1 88
1 90
1 92
2
1 94
1 96
1 98
Depth = 3cm
SGP'97
LSP/R
3 00
Mean Diff = 0.27 K
2
Variance = 2.41 K
2 90
2 80
1 82
1 84
1 86
1 88
1 90
1 92
1 94
1 96
1 98
3 20
3 10
SGP'97
LSP/R
Depth = 10cm
3 00
Mean Diff = 0.03 K
2 90
Variance = 0.75 K
2 80
1 82
(July 1)
1 84
1 86
1 88
1 90
1 92
1 94
Judge et al. 1999
J ulian D ay fro m Ja n. 1(CS T)
1 96
2
1 98
(July 17 )
Correlated Noise Calibration System
From AWG
To Radiometer
Low Noise Amplifier (LNA) input is a
matched source of sub-ambient noise…
it is an electronic device which, at RF,
looks like it is at LN2 temperatures
CNCS concept:
Onto this very low noise background,
couple in some much stronger noise.
This much stronger noise can be generated
in a COTS Arbitrary Waveform Generator
CNCS extension:
This same concept can be used to create
known weak RFI
Ruf and Li, ‘03
Detection and Mitigation Testbed
C-band RFI Detection and Mitigation Testbed
Variable
Bandpass
CNCS Design:
Artificial RFI Generator
Ambient and
Sub-ambient
Calibration
(from CNCS Design)
Variable
Ctr Freq
TMRS-3 Design:
Digital Radiometer
A/D Converter
B1
Vin
GND
FPGA
B8
Vref
Sign
uC
ENB
Flash
Memory
AWG
Personal
Computer
Spectrum
Analyzer
Digital
Scope
Conclusions
Microwave Radiometry has been demonstrated to have high sensitivity
to surface soil moisture.
Hydrologic models can use this measurement
to constrain the evolution of profiles
of temperature and moisture.
This technique should work well
for the low vegetation content
of the Arctic.
Understanding the evolution of the active layer will help us understand
the threat of carbon release from Arctic soils in response to climate change.
Microwave observations are very susceptible to interference.
RFI mitigation for microwave radiometry is an emerging research area at Michigan