Anthropogenic and Natural Sources of Ambient Noise in the Ocean
Detection of Anthropogenic Signals that are Below Thermal Noise Power
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Transcript of Detection of Anthropogenic Signals that are Below Thermal Noise Power
Detection of Anthropogenic Signals that are Below Thermal Noise Power
Roger De Roo
IEEE SEM Fall Conference
2012 Nov 14
Outline
• Motivation– Physics of passive remote sensing
– Monitoring the soil moisture with microwave radiometry
• Radio Frequency Interference: a major problem– Summary of RFI detection approaches
– Kurtosis algorithm development
• Conclusions
• Thanks to: Chris Ruf, U-M; Joel Johnson, OSU; Jeff Piepmeier, NASA Goddard; Sid Misra, JPL
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?
Radar (“Active”)
Radiometry (“Passive”)
Tx
Rx
Rx
Long wavelengths (3mm to 30cm) don’t scatter off of objects the size ofcloud droplets -- microwaves see through clouds
•Very high spatial resolution•Power hungry: expensive•Sensitive to geometry of water: eg. Movement of trees causes big signal changes
•Poor spatial resolution•Low power requirements•Insensitive to geometry of water
Planck Blackbody Radiation
1 GHz 1 THz 1 PHz frequency wavelength 0.3 m 0.3 mm 0.3 um
3K outer space
30K
300K room temp
3000K red hot
6000K white hot the Sun
Microwave Characteristics of the Atmosphere
from LeVine, Wilheit,Murphy and Swift, 1989
Products by frequency
from LeVine, Wilheit,Murphy and Swift, 1989
Also: -Sea surface salinity at 1.4GHz
-Vegetation moisture content at 1.4 and 6 GHz
-Vegetation temperature at 18 – 90 GHz
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 downThe molecule can keep up with the wave until 9 GHz(index of refraction: n = 9 at 1GHz, but n = 2 at 100 GHz)
ε' = n2
Microwave Brightness and Moisture
• 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
Dry Soil~300K
Sensor Space 2.7K
Wet Soil~300K
Sensor
TransparentAtmosphere
TransparentAtmosphere
Low Contrast at Interface High Contrast at Interface
Example Brightness Image from Space
NASDA
Sensitivity of Radiobrightness to Soil Moisture Under a Vegetation Canopy
0.0
1.0
2.0
3.0
4.0
0 5 10 15 20 25Wavelength (cm)
Sen
siti
vity
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B /
V
SM
(K
/%)
BARE
VEGETATIONWATER CONTENT (kg/m2)
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SSM/I AMSR SMAP
Courtesy of P. O’Neill19 GHz 6.9 GHz 1.4 GHz
University of Michigan Radiometers
L-band1.4 GHz = 21 cm
satellites:SMOS Nov ‘09Aquarius Jun ‘11SMAP ‘14
19 GHz 37 GHz = 1.6 cm = 0.8 cm satellites: SSM/I etc. ‘87 to present
C-band6.7 GHz = 4.5 cm
satellites:AMSR-E ’02-’11
Antenna sizeis proportionalto wavelength
The Tundra Landscape
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 MWTransmit Frequency 1.215-1.375GHz
Raytheon
Surrounded by Interfering Radars?
FPS-108Cobra Dane
FPS-117
FPS-124
Observation site:Toolik Lake
ITT, ‘05
AMSR-E Interference at 6.9GHz
Li et al., ’04
If it is not purple, we cannot use the data from that locationIf it is purple, the data from that location might be OK…or not
Traditional Radiometer Technology
• Use square law detector for signal power
k1 v2 v
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Effect of Finite Samples: RFI-free signals
• Variance of voltage waveform contains brightness power:
PIF = kBTSYSB G = <v2(t)>/Z
TSYS = TB + TREC
• Finite number of samples results in a measurement variance:
σ = NEΔT = TSYS / √N N=Bτ
• RFI always biases measurements of TB upwards
– Averaging preserves the bias – thus, not a solution
– We’d like to see RFI at near the NEΔT power level
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
Gaussian pdf Non-Gaussian pdfSinusoidal waveformThermal waveform
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
Literature Search
• What has already been done on this problem, or related problems?
• For RFI mitigation, nothing in the amplitude domain. Some in time-domain 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?
Statistical moments:
• 0th… event count
• 1st… Mean
• 2nd …Variance
• 3rd … Skew
• 4th … Kurtosis
dvvpvnn )(
Skew
• Measures how asymmetric a distribution is
• Normal distribution has zero skew
• So does RFI
Sources of Skew
• No skew for o pulsed sinusoid (ps), o Amplitude Modulation (AM), or o Frequency Modulation (FM)
• Skew possible, but unlikely, with Phase Modulation (PM)
AM FM PM
Kurtosis
• Kurtosis measure “peakedness” of a distribution
• Normal distribution has kurtosis = 3
• RFI can have any kurtosis
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Definition of Kurtosis
• Desired radiometric (science) signals generated by thermal noise– Gaussian (bell-curve) probability distribution function (PDF)
• RFI is man-made – PDFs will be non-Gaussian in general
• Underlying Statistics– all higher-order moments of a Gaussian are uniquely determined by its
lowest two moments
– for example, the kurtosis equals 3 for a Gaussian v(t)
where v(t) is the zero-mean pre-detected radiometer output voltage
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4
)(
)(or
tv
tvR
Technology Approach • Digitized IF waveforms lend themselves to moment
estimation• Use moment ratios to test for presence of RFI• 1st moment, 1, is a DC offset• 2nd central moment, m2, is power – the measurement
objective• Odd central moments are all zero• The lowest moment for RFI detection is the 4th
central moment m4
1 1
Nsv i
i1
N s
m2 1
Nsv i 1 2
i1
N s
m4 1
Nsv i 1 4
i1
N s
ADC vi FPGA
Alternative Technology Approach(explored by Jeff Piepmeier of NASA)
• Use square law detector for signal power
– (traditional radiometer architecture)
• Use a second square law detector for the 4th moment
k1 v2
k2 v4
v
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v
HeA
kdevp k
k
k
v
2
2
12
2
2!
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2
1)(
2
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Expected PDF of thermal noise with variance σ2 with pulsed sinusoidal RFI of amplitude A and duty cycle d: (extension of Rice, 1948)
Probability Densities of signals w/ & w/o RFI
Noise w/ constant power, σ2 RFI w/ Constant Amplitude, A Varying duty cycle, d
=
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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
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Effect of Finite Samples: RFI-free signals
• Exact kurtosis pdf unknown
• Kurtosis pdf is skewed– less so as N∞
– kurtosis pdf is essentially Gaussian N>50k
• Mean of R=3(N-1)/(N+1)
• Variance of R 24/N as N∞
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Detection Concepts
Rth,a
FAR
PD
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False Alarm Rate and Probability of Detection of Pulsed Sinusoidal RFI
• For RFI power level at brightness temperature equivalent to 2NET, detection threshold can be set to give:– 90% probability of
detection– 3% false alarm rate
• 0.1% duty cycle case corresponds to ARSR-1 operating mode
• Higher duty cycle reduces detectability
d is radar duty cycle
PD=1-FAR
z is a FAR parameter
z=3 FAR=0.25%
z=2 FAR= 5%
z=1 FAR=30%
For large N,
min detectable RFI
TPS ~ N-¼
NEDT ~ N-½
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Minimum detectable RFI
“Blind Spot” at 50% duty cycle, and solution
CFAR R4 (kurtosis)
CFAR R6
13%blindspot
61%blindspot
50%blindspot
R6=+0.085R6= -0.085
R6=+0.085
R 4=+0.0155
R4=-0.0155
0155.01
0849.01
10024
100720
k
k
N=100kSaThreshold at 1σ (30% FAR)
Laboratory Experiments
• Check assumptions about radiometer operation
• RFI is prescribed to conform to our theory’s assumptions
VariableAttenuator
ArbitraryWaveformGenerator
DetMit Rcvr
CNCS ControlADD Data AcqADD
CNCS RF head
Laboratory Experiment Results
• Kurtosis R= m4/m22
• In the absence of RFI, R=3• For CW RFI (eg. Carriers) R<3• For short duty cycle RFI (eg. RADAR), R>3• But R=3 also for 50% duty cycle
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Kurtosis of RFI free signals• Kurtosis False Alarm
Rate confirmed with
simulations
• PALS-ADD data:
a minute of apparently
RFI free data
• RFI free
assumption
supported
by kurtosis FAR
predictions
z = |Rth-3| / σR0
Theoretical FAR=1-erf( z /√2)
RFI flags from clean PALS-ADD data
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Kurtosis of the RFI-free: digitization effects• Effects considered (and are very small):
– Kurtosis pdf itself is Gaussian for 50k+ independent samples
– Clipping of signals by finite Analog to Digital Converter (ADC) dynamic range: 4 bits is enough; 3 bits, maybe
– Digitization (ADC bin size) effects are negligible.
– ADC null offsets can be corrected with 1st and 3rd moments in addition to 2nd and 4th moments needed for kurtosis algorithm.
• Effects not yet considered:
– Integral Nonlinearity and Differential Nonlinearity of Analog to Digital Converters: likely is small effect because Flash ADC typically have small INL and DNL
– Correlated data: we are still applying the theoretical tools to analyze the effects of sampling above the Nyquist rate on the kurtosis calculation.
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Kurtosis of RFI-free: effects of digitization
• Digitization reduces kurtosis• Bin size effects decrease as noise amplitude increases• Threshold locations not critical for s>3/4• Saturation of ADC at high noise amplitude distorts kurtosis
Number of ADC bins
s = 3/4
Field Experiments
• A lot of fun to do!
• Takes lots of people ($$$) to do.
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Example of RFI detection with Kurtosis (1)
• 1 minute of data from ADD back-end attached to PALS front-end at JPL
• Antenna looking to sky
• Kurtosis thresholds set to trigger 1 false alarm per minute
• Flagged observations: some obvious RFI, some not
seconds
TSY
S (
coun
ts2 )
RFI flags
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Example of RFI detection with Kurtosis (2)
• Another PALS minute of data; same kurtosis thresholds
• Antenna looking to sky; absorber placed over antenna
• Changes in brightness do not get flagged
seconds
TSY
S (
coun
ts2 )
absorbersky sky
RFI flags
Airborne Campaign Results
Soil Moisture: Active and Passive (SMAP)
NASA environmental satellite
Currently in planning stages
Launch Nov 2014
Kurtosis is the main RFI detection algorithm
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Conclusions
• Theoretical behavior of the kurtosis statistic as a detector of pulsed sinusoidal RFI has been explored.
– Kurtosis has a blind spot at 50% duty cycle sinusoids
– CW RFI lowers kurtosis below 3
– Low duty cycle sinusoidal pulses raise the kurtosis above 3
– Kurtosis is very sensitive to low duty cycle sinusoid pulses
– Kurtosis is minimally affected by digital receiver properties
– False Alarm Rate of kurtosis algorithm is confirmed
– Minimum detectable RFI is comparable to NEDT in realistic circumstances, may be less than NEDT
– Kurtosis false alarms do not bias the estimate of the brightness
– The kurtosis does not flag gradual changes in brightness.
Thank You!
Backup Slides
ADC offset and non-central moments
• offset in ADC “ground” requires 4 moments • 3rd moment of questionable value• elimination of 3rd moment can
• relax back-end data rate requirements,• allow more subbands, and/or • permit shorter integration periods
s
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vi=+1
vi= 0
vi= -1
vi= -2
Tanana River Breakup at Nenana
Guess the moment of breakup!Tickets cost $2.50 eachTypical Jackpot: $300,000+
www.nenanaakiceclassic.com
Spring Breakup
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1900 1920 1940 1960 1980 2000 2020
Year
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Observed Global Temperature Trends
IPCC ‘01
Projected Global Temperature Trends
IPCC ‘01
2071-2100 temperatures relative to 1961-1990.Special Report on Emissions Scenarios Storyline B2 (middle of the road warming).
Carbon Stocks by Biome
IPCC ‘01
Atmospheric stock is about 750PgC
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Tropic
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Tundr
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plants
soils
Permafrost extent
Global Terrestrial Network for Permafrost
20m Borehole Temperature Trends in AK
Hinzman et al 2005
Permafrost structure
NSIDC
Active Layer Depth Trends
Circumpolar Active Layer Monitoring Network
Year
Max
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ive
Lay
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epth
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Strategy for Estimating Temperature and Moisture Profiles
Atmospheric Model
Weather & downwelling radiance
SVAT Model
Temperature & Moisture Profiles
Radiobrightness Model
Satellite L-band Radiometer
Tb (model)
Tb(observed)
Assimilate Tb(observed) - Tb(model)
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SGP'97 (TIR) LSP/R (Canopy)
LSP-SGP = -0.28 K
Variance = 3.28 K2
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SGP'97 LSP/R
Depth = 3cm
Mean Diff = 0.27 K
Variance = 2.41 K2
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198196194192190188186184182
Julian Day from Jan. 1(CST)
SGP'97 LSP/R
Depth = 10cm
(July 1) (July 17)
Mean Diff = 0.03 K
Variance = 0.75 K2
Calibrated LSP/R model of Prairie Grassland
Judge et al. 1999
Correlated Noise Calibration System
Ruf and Li, ‘03
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
From AWG To Radiometer
Detection and Mitigation Testbed
C-band RFI Detection and Mitigation TestbedVariable
Bandpass
Ambient andSub-ambient Calibration
(from CNCS Design)
CNCS Design:Artificial RFI Generator
AWG SpectrumAnalyzer
Digital Scope
PersonalComputer
VariableCtr Freq
FlashMemory
TMRS-3 Design:Digital Radiometer
Vin
GND
Vref
B1
B8
Sign
ENB
A/D Converter
FPGA
uC
ConclusionsMicrowave 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
Microwave Brightness and Moisture• Water molecules have large electric dipole, unlike rest of nature
• 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.
H- O + H
Space 2.7K
Dry Soil~300K
Sensor Space 2.7K
Wet Soil~300K
Sensor
Liquid water molecules will orient itself with passing electromagnetic waves, slowing the wave downThe molecule can keep up with the wave until 9 GHz(index of refraction: n = 9 at 1GHz, but n = 2 at 100 GHz)
TransparentAtmosphere
TransparentAtmosphere
Low Contrast at Interface High Contrast at Interface