remote sensing in severe radiation environments sensing in severe... · 2012. 10. 26. · remote...

Remote Sensing in Severe Radiation Environments

Ralph Levy*, Kevin P. Hand**, Robert W. Carlson**Winthrop Wadsworth***, Jens Peter Dybwad***

Daniel Berisford**, Didier Keymeulen**, Jason E. Feldman**

•Quant Engineering, ** NASA JPL, *** D&P Ins truments

•Presented at: 2011 Sensors Tech Forum, Boston, M A

Overview• Based on a spectrometer for the delayed NASA Jupiter Europa

Orbiter mission

• Why go to Jupiter - possibilities and problems

• FTIR Spectral Instrumentation

• Analysis and Removal of Radiation Effects

• Other Applications

• Work funded by: NASA JPL contract #1396649Edgewood Chemical and Biological Center DAAD13-03-C-0035 Quant Engineering internal funding

Moons of Jupiter with Liquid Water

Measurements and Problems

WAVELENGTH, µm

0 1 2 3 4 5 6 7 8 9 10 11 12

RA

DIA

NC

E, 1

09 p

hoto

ns s

-1 c

m-2

ste

r-1 (

cm-1

)-1

1

10

100

1000

31%

23%

15%

10% (µ0 > 0.2)

120 K

110 K

100 K

130 K

θ0 = 67.5

θ0 = 0

• What we want to measure What happens when we try

Radiation Environment at Europa

CHARGE, fC

1 10

DIF

FE

RE

NT

IAL

HIT

RA

TE

, s-1

fC-1

0.1

1

10

100

1000

EUROPA G1ENHiLatInSb, DETECTOR NO.14

AVERAGE CHARGE

D&P Instruments TurboFT• Very rugged, only one moving (rotary) part• Data acquisition triggering• Spectral resolution is a function of the rotor

thickness and rotor material index of refraction• 4 quadrants for design purposes

FTIR Spectrometer• Multiplexing (Felgett) advantage, light at all wavelengths is collected

simultaneously.

• Higher light-gathering power than dispersive spectrometers (Jacquinot advantage). TurboFT (D&P Instruments) is approximately 50 × more sensitive in light gathering than the Galileo Near Infrared Mapping Spectrometer (NIMS).

• Built-in Radiation tolerance – Noise is spread and apodization

• AC-coupled to data acquisition

• Signal Processing can greatly increase performance in high radiation environment

Data Analysis for the TurboFT Spectrometer

• Peculiarities of the TurboFT – 4 quadrants

• Compute spectra by FFT

• Co-Add spectra for each quadrant

• Combine for a single spectrum

Data Rates and Resolution

• Acquire multiple scans– Between 10 and 360 scans per second – Multi-pixel versions

• Good spectral resolution 8 cm-1 (4096 interferogram data points), tighter if desired.

• At slow rotation speeds (10 scans/sec) the single-pixel data rate is approximately 1 MB/sec.

Integration Times and Data Redundancy

• Integration times estimated at approximately 60 seconds per physical location at Europa

• With 4 rotational positions, there are 150 (60*10/4) samples of each interferogram data point

• Time interval between successive interferogram points is approximately 8 us

Requirements for Signal-to-Noise are dependent on analysis

• Single wavelength analysis– Often what is taught at school– Deservedly bad reputation

• Integrated peaks– A modest improvement

• Spectral Methods– If there is structure in the target spectrum– Demonstrated SNR < 1 with high accuracy

• This talk is not about Spectral Processing Methods but about separable signal and noise in the measurement – a prelude to Spectral Processing

Statement of the Problem

• Each Interferogram is a sampling of the same signal so there are redundant samples

• Each sample contains signal and ambient noise measured together

• Because the data rate is fast compared to the radiation noise frequency near Jupiter, the “signal” mean can be recovered

Indexed Statistics

• Simple idea – Powerful in practice: Throw out what is inconsistent to find

what you are looking for

• Start with the median value of the distribution and compute mean value and standard deviation, throw out samples that are outside 2 sigma limits

• Repeat by re-sampling remaining data until convergence (usually

Signals and NoiseRange of Application and Limitations

• Signals must be separable:– Standard deviation of “signal” must be small

compared to magnitude of noise

• Examples:– Signal with Big noise– Signal with White noise– Signal with Small noise– Signal with Europa noise

Signal, Noise and MeasurementBig Noise

Normalized Histogram of Signal, Noise and Measureme ntSignal Mean at 10.0 - Idx Calculated Mean = 10.01 4

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0 5 10 15 20 25 30

Signal Intensity

Rel

ativ

e F

requ

ency

of O

ccur

renc

e Signal

Noise

Measurement Average = 15.09

Signal, Noise and Measurement Data

0

5

10

15

20

25

30

0 100 200 300 400 500 600

Sample Number

Sam

ple

Val

ue

Signal, Noise and MeasurementWhite Noise


0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0 5 10 15 20 25 30

Signal Intensity

Rel

ativ

e F

requ

ency

of O

ccur

renc

e

Signal

Noise



0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600

Sample Number

Sam

ple

Val

ue

Signal, Noise and MeasurementSmall Noise


0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0 5 10 15 20 25 30

Signal Intensity

Rel

ativ

e F

requ

ency

of O

ccur

renc

e

Signal

Noise



-5

0

5

10

15

20

0 100 200 300 400 500 600

Sample Number

Sam

ple

Val

ue

Signal, Noise and MeasurementEuropa Noise (synthetic)


0.0

0.5

1.0

1.5

2.0

2.5

0 100 200 300 400 500 600

Sample Number

Sam

ple

Val

ue

Normalized Histogram of Signal, Noise and Measureme nt Signal Mean at 0.50 - Idx Calculated Mean = 0.497

0

0.1

0.2

0.3

0.4

0.5

0.6

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

Signal Intensity

Rel

ativ

e F

requ

ency

of

Occ

urre

nce

Signal

Noise

Measurement - Mean = 0.79

Interferogram Jitter and Registration

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

1950 1970 1990 2010 2030 2050 2070 2090

Interferogram Sequence Point

Signa

l Inten

sity / Volt

-1

0

1

0 500 1000 1500 2000 2500 3000 3500 4000

Inteferogram Data Point Sequence Number

Radiation Effects – 14 Interferograms

-1

0

1

2

3

4

5

6

7

8

0 500 1000 1500 2000 2500 3000 3500 4000


Vol

tage

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

1900 1920 1940 1960 1980 2000 2020 2040 2060 2080 2100


Vol

tage

-1

0

1

0 500 1000 1500 2000 2500 3000 3500 4000


2 Sigma Limits

Registered

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

1950 1970 1990 2010 2030 2050 2070 2090


Sig

nal I

nten

sity

/ V

olt

Unregistered

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

1950 1970 1990 2010 2030 2050 2070 2090


Sig

nal I

nten

sity

/ V

olt

Application to Radiation in FTIR Data

0.0000

0.0002

0.0004

0.0006

0.0008

0.0010

0.0012

0.0014

0.0016

0.0018

0.0020

4 6 8 10 12 14 16 18 20

Wavelength - um

Sig

nal I

nten

sity

With Radiation

Without Radiation

Repaired

Another Example

0.0004

0.0005

0.0006

0.0007

0.0008

0.0009

0.0010

0.0011

0.0012

6.2 6.4 6.6 6.8 7.0 7.2 7.4 7.6 7.8 8.0

Wavelength - um

Sig

nal I

nten

sity

Radiation

New

Fixed

Removed fromIntegral

Integral from Wavelength = 6.5702 to 6.8196 um above the black trapezoid is: 0.0000129 for the original spectrum and 0.0000125 for the spectrum with Radiation that had been Fixed - 3.2% error in the feature bump

An Inverse Application to Curve Fitting of Noisy Data

• Fluorescence Background Removal in RamanSpectroscopy – where this numerical technique was originally developed

– Compute fluorescence signal, e.g., as Gaussian (3 variables)

– Compute Error and throw out big points with large error (Raman signal)

– Optimize fit of Gaussian to reduced data set

Removal of Raman Background Fluorescence

-1.0

0.0

1.0

2.0

3.0

4.0

5.0

6.0

0 1 2 3 4 5 6 7 8

Wavelength (arbitrary units)

Sig

nal I

nten

sity

Measured Signal

Computed FluorescenceBackground

Computed Raman Signal

Take-Aways

• FTIR has inherent tolerance for Radiation

• Instrument design can influence operation

• Data analysis can pull much information out of some types of noise

JPL Jovian References• http://opfm.jpl.nasa.gov/ • http://opfm.jpl.nasa.gov/europajupitersystemmissionejsm/ejsmpresentations• Boldt, J., et al., 2008. Assesment of Radiation Effects on Science and

Engineering Detectors for the JEO Mission Study. Jupiter Europa Orbiter Mission Study 2008: Final Report. JPL D-48256

• Carlson, R. W., 2010. Radiation Noise Effects at Jupiter: Comparison of In-situ and Laboratory Measurements RTD Final Report. Jet Propulsion Laboratory, Pasadena.

• Carlson, R. W., et al., 2009. Europa's Surface Composition. In: EUROPA (R. T. Pappalardo, et al., Eds.). Univ. Ariz. Press, Tucson, 283-327.

• Fieseler, P. D., et al., 2002. The radiation effects on Galileo spacecraft systems at Jupiter. IEEE Transactions on Nuclear Science. 49, 2739-2758.

• Hand, K. P., et al., 2009. Astrobiology and the Potential for Life on Europa. In: EUROPA (R. T. Pappalardo, et al., Eds.). Univ. Ariz. Press, Tucson, 589-630.

TurboFT References• Wadsworth, W., Dybwad, J. P., 1997. Ultra high speed chemical imaging spectrometer.

ElectroOptical Technology for Remote Chemical Detection and Identification, Vol. 3082. Soc. Photog. Instrum. Eng., pp. 148-154.

• Wadsworth, W., Dybwad, J. P., 1998. A very fast imaging FT spectrometer for on line process monitoring and control. Electro-Optic, Integrated Optic, and Electronic Technologies for Online Chemical Process Monitoring, Vol. 3537. Soc. Photog. Instrum. Eng., pp. 54-61

• Wadsworth, W. and Dybwad, J.P., 2001a, Airborne Testing of Small, Fast, Rugged Fourier Transform Spectrometer for Geologic Survey Use, Proceedings of Fifth International Airborne Remote Sensing Conference, San Francisco, CA, 17-20 September, 2001.

• Wadsworth, W., Dybwad, J. P., 2001b. Field testing of a small, fast, rugged Fourier transform spectrometer in the air and on the ground. ISSSR 2001, Quebec City, Canada.

• Wadsworth, W., Dybwad, J. P., 2001c. Rugged high speed rotary imaging Fourier transform spectrometer for industrial use. Vol. 4577. Soc. Photog. Instrum. Eng.

• Winthrop Wadsworth, "8x8 element mosaic imaging FT-IR for passive standoff detection“, 7th 2006 Standoff Detection Conference in Williamsburg, VA, 23-27 October 2006, Proc. SPIE 6302, 630202 (2006)

• "8X8 Element Mosaic Imaging FT-IR for Passive Standoff Detection“, SPIE Optics & Photonics Conference in San Diego, 13-17 August, 2006

• Hewson, R., et al, Hyperspectral Thermal Infrared Line Profiling for Mapping Surface Mineralogy, Proceedings of Fifth International Airborne Remote Sensing Conference, San Francisco, CA, 17-20 September, 2001.

• http://www.dpinstruments.com/publications.php

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