4. miller - remote sensing and imaging

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Remote Sensing and ImagingPhysics16 March 2011

Kent MillerProgram Manager

AFOSR/RSE

Air Force Office of Scientific Research

AFOSR

Distribution A: Approved for public release; distribution is unlimited. 88ABW-2011-0750


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2011 AFOSR Spring Review2301F Portfolio Overview

NAME: Kent Miller

BRIEF DESCRIPTION OF PORTFOLIO:Understand the physics that enables space situational awarenessUnderstand the propagation of electromagnetic radiation and theformation of images

LIST SUB-AREAS IN PORTFOLIO:

1. Image Formation and Processing

2. EM Propagation and Imaging through Deep Optical Turbulence

3. Identification of Unresolved Space Objects4. Predicting the Location of Space Objects

5. Student Programs: University NanoSats, Space & DE Scholars


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Scientific Challenges


What new information sources give higher resolution2. EM Propagation and Imaging through Deep Optical Turbulence

What physics describes amplitude singularities (branchpoints)

3. Identification of Unresolved Space Objects How to fingerprint a satellite we cant image if we cant

deconvolve the spectrum

4. Predicting the Location of Space Objects

How to predict future location of satellite How to ID thousand of new objects when new sensors come

on line

5. Student Programs: University NanoSats, Space & DE Scholars

How to attract the brightest students


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Transformational Opportunities


More rapid, more accurate image reconstruction2. EM Propagation and Imaging through Deep Optical Turbulence

New models for EM propagation and turbulence with strongamplitude scintillation

3. Identification of Unresolved Space Objects Breakthrough in spacecraft materials characterization

4. Predicting the Location of Space Objects

Rapid orbit determination transformational capability to dealwith 300,000 newly observed RSOs


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Transitions from RecentProjects

Adaptive control of effects of turbulence and jitter onairborne laser platforms to JTO and AFRL

Fast, accurate gravity field model, to AFSPC

Holographic AO - Eliminates the computer required todeform mirror to compensate wavefront distortions

Laser Cooling reduce need for bulky, noisycryocoolers Transitioning to RV through an STTR

Have reached 110K, expect 70K soon


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From Surveillance to SSA


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The Physics ofSpace Situational Awareness

Complex problems includes research from severalprogram managers as well as most AFRL directorates

Requires cross-discipline research to turn SpaceSurveillance, Astrodynamics, Space Weather,Information Sciences, Electromagnetics, etc. intoSpace Situational Awareness

Imaging andSurveillance Situational Modeling

EnvironmentalEffects


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1. Imaging


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Atmospheric Turbulence

No turbulence Turbulence

Star

Atmosphere

Telescope

Star image(Point Spread

Function)

Light from star

Typical Imaging Conditionsat 0.5 m at AMOS

AO compensation

AOcompensation

+ post-processing

D/r0= 10

D/r0= 20

R t ti f D t


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Restoration of DataStrong Atmospheric Turbulence

Target turbulence levels : 50 D/r0 80

[ Daytime conditions at AMOS ]

Extend the range of conditions for acquisition ofhigh-fidelity imagery

Important class of satellites that can only be observedaround noon local time

Simulations of the Hubble Space Telescope as it would appear from the 3.6 m AEOS telescope at a range of 700

km in 1 ms exposures at 0.9 m wavelength under a range of seeing conditions.

Pristine D/r0 = 5 D/r0 = 20 D/r0 = 100

8 arc sec

Stuart Jefferies, University of HawaiiJames Nagy, Emory University


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Image Post Processing

AFOSR award F9550-09-1-0216

DWFS and Frozen Flow Model1) Wave Front Sensor data available

Restoration of daytime imagerynow feasible

D/r0=100

Compact MFBD (CMFBD)2) No Wave Front Sensor data

Data MFBD CMFBDCMFBD

MFBD

Stuart Jefferies, University of Hawaii

James Nagy, Emory University


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Aperture Partitioning

Imaging through atmospheric turbulence

r0small relative to DAperture contains many atmospheric cellsBaseline redundancy causes turbulence noise in the

bispectrum estimate.

New approach:

Partition the pupil into concentric annuliFocus each region on a separate camera

No photons discarded - critical for dim objects

Reduces baseline redundancy noise, improves image

Telescope pupil

Camera 1

Camera 2

Camera 3

Full aperture Partitioned aperture

high

low

Bispectrum SNR is improvedFull aperture Partitioned aperture

Reconstructed image is improved

Dr0

Telescopeaperture

Atmosphericseeing cells

Brandoch Calef, AFRL/RD


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2. Imaging in Extreme AtmosphericSeeing Conditions

Astronomy good seeing, generallyfavorable zenith angles, operations exclusivelyat night

Military possibly unfavorable seeing, zenithangles, both day/night operations desired.

Users are asking for more:

Daytime imaging

Imaging un-illuminatedobjects in infrared

Operations at very lowelevation angles

Tactical time frames

Control of laser beams

through turbulence

P i d I i h h


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Non-Kolmogorov Processes

Radiative HeatingConvection

gravity waves

DiffractionLimit

Beacon Size

IsoplanaticAngle

Propagation and Imaging throughDeep Turbulence

Tens of kilometers in moderate turbulence Small isoplanatic angle Branch points Atmospheric guiding Laser speckle spoofs WFS;

reduces power

Classical single beacon will not extend therange beyond 50 km

The Science Advisory Board (SAB)

challenged AFRL/RD to solve beamcontrol for horizontal paths

Th C ti d E l ti


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The Creation and Evolutionof Branch Points

Causality

Causality prevents the wave changing

instantaneously across all space when

evolving from time to time

Branch point phase is given by

Causality precludes branch points from

forming unless ... pairs of opposite polarity

Results

BP creation pair

Novel approach in studying the new

regime yielded nice initial result

Opened the door for many more results

Branch points:

Created in pairs of

opposite polarity

infinitesimally close

together Creation pairs

evolve smoothly

with propagation

Creation pairs have

the velocity of the

turbulent layer

Darryl Sanchez, ASALT Lab, AFRL/RD


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Beam Propagation

Haleakala (near AMOS)

Mauna Loa

Mauna Kea Beacon

Classical atmosphericturbulence theory:

Developed 1940s 1960s

Short paths, close to ground

Predicts correlated powerlevels at different wavelength

.

PH= Taer Tprop P0

Propagation

transmittance

Power at

the receiver

Beacon

output

powerAerosol

transmittance

(0.53)

Received Beam Characteristics:

Rao Gudimetla, AFRL/RD

Mikhail Vorontsov, University of Dayton

P Fl t ti f


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Power Fluctuations ofReceived Beacon Light

2dn n n SP I S I r r rPower:IR: = 1.06 m (red lines); Vis.: = 0.53 m (green lines)COM: = 1.5 m (blue lines)

3 beacon_C: 2/16/2010; 9:50 p.m.

max ( )n n nP P P 3 beacons 19: 2/13/2010; 10:30 p.m.

0 10 20 30 40 50

1550 nm

1064 nm

532 nm

0 2000 4000 6000 8000 10000

0 2000 4000 6000 8000 10000

0.00

0.50

1.00

0.00

0.50

1.00

0 2000 4000 6000 8000 10000

0.00

0.50

1.00

0 10 20 30 40 50

1550 nm

1064 nm

532 nm

0.00

0.50

1.00

0 2000 4000 6000 8000 10000

0.00

0.50

1.00

0 2000 4000 6000 8000 10000

0.00

0.50

1.00

0 2000 4000 6000 8000 10000

sec sec

SP2SP1

DS1 DS2

SP3 SP4

Rao Gudimetla, AFRL/RDMikhail Vorontsov, University

of Dayton


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3. Non-Resolved Space Object ID


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Joint Segmentation and Reconstructionfrom Multispectral Data

Raw SD-CASSI simulated image, iterative

reconstructions Eight HST materials from NASA as spectral

signatures

Alternating segmentation and reconstructionusing variational methods lead to excellent

recovery of hyperspectral datacube Look for jumps in the spectrally-integrated

fluxes easily obtained via a local gradient map

Black True NASA signaturesGray NMU based on I2normDashed NMU based on I1 normfor fit-to-data approximation

Resulting Segmentation

Doug Hope, University of New Mexico

Sudhakar Prasad, University of New MexicoDavid Brady, Duke University

Unresolved Target Discrimination from


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Unresolved Target Discrimination fromSpatial Distribution of Polarization

Different

target

material

Data acquisition classification Target condition

discrimination

unresolved

polarization

analyzer

10 20 30 40 50

5

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35

40

45

50

5 10 15 20 25 30 35 40

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100 200 300 400 500

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450

5005 10 15 20 25 30 35 40 45

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rough metallic surface kaolin diffuse coating

polyvinylidenecellulosemembrane

0 0. 1 0 .2 0. 3 0 .4 0. 5 0 .6 0. 7 0 .8 0. 9 10

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

CDMP value

0.65 0.7 0. 75 0.8 0. 85 0. 9 0. 95 10

5

10

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25

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40

CDMP value

0 0.1 0. 2 0.3 0.4 0. 5 0 .6 0.7 0. 8 0 .9 10

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

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CDMP value

0 0. 1 0 .2 0. 3 0 .4 0.5 0. 6 0 .7 0.8 0. 9 10

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

CDMP value

The field is locallypolarized !!

Complex Degree of Mutual Polarization (CDMP)

measure of similarity between the states of polarization at twodifferent points

provides information above and beyond intensity distribution

jijijijirefrefrefref

jirefjiref

EyEyExExEyEyExEx

EyEyExExCDMP

,

*

,,

*

,

**

2

,

*

,

*

Scattered field(speckle)

CorrespondingCDMP

histogram

Useful when material discrimination basedon intensity distributions is impossible

It provides fast, one-shot materialcharacterization / target discrimination

CDMP is a robust higher order correlator

discriminator

Aristide Dogariu, CREOL, UCFOpt. Express 18, 20105 (2010)

P ti l M ll P l i t f


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Partial Mueller Polarimetry forTarget Detection

A partial Mueller polarimeter makes

fewer than 16 measurements (2

measurements in the case shown) to

affect a polarimetric detection.

Decreasing Depolarization

Increasin

gContrast

2-measurepartial Mueller

1-measurepartial Mueller

Unpolarized

Passive

polarized

From: F. Goudail and J. S. Tyo, When is polarimetric imagingpreferable to intensity imaging for target detection, JOSA A, Jan 2011

Using a partial polarimeter to

differentiate laser damage on a

target sample

Scott Tyo, University of Arizona

Sky Polarization


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New capability to accurately model :

All-sky images of sky radiance (top)

Degree of linear polarization (DoLP, bottom)

Sky PolarizationMeasurements and Models

0 0.2 0.4 0.6 0.8 10

0.2

0.4

0.6

0.8

1

Polarimeter

SOSM

odel

Maximum DoLP: 450 nm

0 0.2 0.4 0.6 0.8 1

0

0.2

0.4

0.6

0.8

1

Polarimeter

SOSModel

Maximum DoLP: 450 nm

Measurement Model

A 5% increase of the real part of the aerosol

refractive index removes a significant bias inscatterplots of measured and modeled DoLP.

Joseph Shaw, Nathan J. Pust, Andrew R. Dahlberg, Montana State University

4 Predicting the Location of


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4. Predicting the Location ofSpace Objects

~2025Mar 2007~1957-61

Uncertainty Recovery and Prediction of


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Uncertainty Recovery and Prediction ofOrbital Dynamical Systems

Modern information-theoreticapproaches to space

surveillance require Substantial increases in computational

requirements for correctly representinguncertainties;

Fast, accurate propagation of orbitaltrajectories.

Numerica/CU STTR

demonstrated the potential of

Realistic State and Measurement Error

Uncertainty Computation and Propagation

A new class of highly efficient A-stablesymplectic orbital propagators providingcentimeter accuracy over many orbitalperiods;

A new gravity model shown to be 3-4 faster

than traditional spherical harmonics.Aubrey Poore, Joshua Horwood, Numerica Corp.


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5. University NanoSatellite Program

27 Universities and More Than 4500 Students Since 1999
http://www.colorado.edu/


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FASTRAC Launch

Launched in Nov 2010 by the SpaceTest Program

Launched on a Minotaur IV rocket to a650 km orbit

Launched with six other spacecrafts

Perfect launch and deployment!Pictures by David Voss

University NanoSatellite


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2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

University NanoSatelliteTimeline

NANOSAT-1/-2Kick-off

NANOSAT-3Kick-off

NS-2 LaunchDelta IV Heavy

NS-3 DownselectUT-Austin (FASTRAC)

NS-2 Delivery3-Corner Sat

NS-2LV Integration

NS-3 DeliveryNS-3 Launch

NANOSAT-4 Kick-off

NS-4 DownselectCornell (CUSat) NS-4 Delivery

NANOSAT-5 Kick-off

NS-5Downselect

NANOSAT-6 Kick-off

NS- 6Delivery

NS-4 Launch

NS-5Delivery

NS-5Launch

NS-6Downselect

NS-6Launch

NANOSAT-7 Kick-off

NS-7Downselect


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Air University: The Intellectual and Leadership Center of the Air Force

Aim High Fly, Fight, and Win

The AFIT of Today is the Air Force of Tomorrow.

AFITs Ground Station

Control of AFITs CubeSats

Control of AFITs observatories

Utilize Common Ground Architecture (CGA) Software to allow

command and control of multiple CubeSats concurrently as well

as permit lights out autonomous operations.

Capability exists at USAFA, USMA, and in the near term at VAFB

Flying FalconSAT-3

currently

Will be a new learning

tool for all future 1.3s

as part of Space 100training

Program started with

AFOSR funding

Space Scholars


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Space ScholarsSelected Research Projects

Design of a Large Strain Joint Deployable Solar

Panel Mechanism for Cubesats

Student: Karl Brandt

Mentor: Tom Murphey

6DOF Orbit and Attitude Simulations for Plugand Play Controller Development

Student: Benjamin Hanna

Mentor: Capt Doug McFarland

Stowage and Deployment Strength of Rollable

Composite Shell Reflectors

Student: Tyler Keil

Mentor: Jeremy Banik

Space Debris Detection in the SMEI Data Archive

Student: Alessa MakuchMentor: Kathleen Kraemer

Enabling Technologies for Electrodynamic

Tethers and Charge Control

Student: Matthew Knoll

Mentor: David Cooke

Physical Characteristics of Flare Associated

Sequential Chromospheric Brightenings Student: Michael Kirk

Mentor: K. Balasubramaniam

Q ti ?


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