Slide 1

Multimodal Data and Anomaly Detection in SSA at AMOS15 Oct 2012Dr. Keith KnoxAir Force Maui Optical & Supercomputing SiteMaui, HawaiiPhoto ofBrieferLead ~ Discover ~ Develop ~ DeliverAir Force Research LaboratorySAB Review FY11: TECHNOLOGYUPDATE: 2010 Sep 8 -- DISTRO CAFRL/Directed Energy Directorate 1Air Force Maui Optical & Super Computing Site2

Movie: AMOS zoom videoAMOS Conference 20089/11/2008 UpdateGar Hassal2AFRL/PA RD 08-0075Air Force Maui Optical & Supercomputing SiteLargest telescope in Department of Defense with 3.6m primary telescopeHighest resolution adaptive optics in Department of DefenseLargest electro-optical tracking facility in the Pacific1960:The Advanced Research Projects Agency (ARPA) Midcourse Optical Station1963:Site construction started by ARPA1994:High Performance Computer Center (HPCC) completed1999:Advanced Electro-Optical System (AEOS) completed2001:Air Force Research Laboratory 50 Years of Service to the Department of Defense

3AMOS Conference 20089/11/2008 UpdateGar Hassal3AFRL/PA RD 08-0075

Air Force Research Laboratory Directed Energy DirectorateMaui Space Surveillance SystemMSSS (AFRL)3.6m and 1.6m telescopesMaui Space Surveillance System

High-resolution ImagingOrbital TrackingSpace Object Characterization

GEODSS

1.6m4AEOS3.6mGEODSS (AF Space Command)Ground-based Electro-Optical Deep Space Surveillance

AMOS Conference 20089/11/2008 UpdateGar Hassal4AFRL/PA RD 08-00753.6-meter TelescopeAdvanced Electro-Optical SystemAEOS 3.6 Telescope

5AMOS Conference 20089/11/2008 UpdateGar Hassal5AFRL/PA RD 08-00751.6-meter Telescope1.6 Meter Telescope Inside Dome

6AMOS Conference 20089/11/2008 UpdateGar Hassal6AFRL/PA RD 08-0075Adaptive Optics ImagingAEOS Visible Imager

Hubble Space TelescopeAdaptive Optics (AO) plus multi-frame blind deconvolution processing Terminator Imagery

DayNightTerminator7AMOS Conference 20089/11/2008 UpdateGar Hassal7AFRL/PA RD 08-0075Long-Wave Infrared ImagingResolved thermal imagesVirtually diffraction-limited

Nighttime Imagery

DayNightTerminatorAEOS Infrared Imager8AMOS Conference 20089/11/2008 UpdateGar Hassal8AFRL/PA RD 08-0075Speckle Imaging Daytime Imagery Terminator ImagerySpeckle Imaging 1.6m

Raw dataProcessed Result

DayNightTerminator9AMOS Conference 20089/11/2008 UpdateGar Hassal9AFRL/PA RD 08-0075

As satellite image size decreases Smaller satelliteGreater distance

the satellite becomes completely unresolvedSatellites in geo orbitCubesat-class satellitesNon-imaging Characterization1010Resolved optical imagery is arguably the easiest for an analyst to interpret. One immediately determines information about the satellite, including whether its operational, and its function. But as the resolution decreases, either because the satellite is too far away, or too small, the resolution decreases to an elongated blob, and finally just a point of light.

Nevertheless, it is still important to characterize these unresolved satellites. Satellites that fall into this category include geo satellites, which are of particular interest today, or Cubesat satellites, which demonstrate the ability to pack many capabilities into a small, unresolvable package.

This need shows up in current requirements documents, including not just the ability to determine features, but also to detect changes in those features, and to support anomaly identification.Non-Imaging TechniqueTemporal filter photometryMeasured brightness as function of timeFor an object facet to contribute to signalFacet must be illuminated by SunFacet must be visible to sensorSensor requirements are simpleCalibrated light bucket

Temporal photometry

11The technique I will talk about today does not depend on a priori knowledge of the satellite. It relies on filter photometry, that is, brightness as a function of time.

As the upper image indicates, the satellite is illuminated by the Sun. The reflected sunlight is detected at some observing location. All 3 of these are moving with respect to one another, which provides a useful diversity of illumination conditions.

With no loss in generality, one can consider the exterior of any satellite to be made up of facets. For any facet to contribute to the observed signal, it must be illuminated by the Sun, and visible to the observer. If not, it does not contribute to the observed brightness.

Its important to realize that the sensor requirements for this technique are minimal. We do not require sophisticated instrumentation, and we do not need large apertures. We simply need a light bucket to collect photons.

For a light curve as shown on the lower image, there is a wealth of information in every feature, whether its the presence of a peak or valley, the width of that feature, the location of that feature, or the structure between major features.The physics is simple. We just need to decode that signal to understand the structure and motion of the satellite.

SAB Review FY11: TECHNOLOGYUPDATE: 2010 Sep 8 -- DISTRO CAFRL/Directed Energy Directorate 11Astrodynamics & TrackingHigh Performance Computing Software Applications Institute for Space Situational AwarenessThe Institute meets these challenges by bringing together:Supercomputing expertiseWorld-class researchers from AFRL

Good orbit knowledge and some status infoAccurate orbits and uncertainty

Object identification status and healthNow15k ObjectsFuture150k Objects12The Institute works directly with the research, operations, and acquisition communities to develop and transition HPC software applications that provide game-changing SSA capabilitiesAMOS Conference 20089/11/2008 UpdateGar Hassal12AFRL/PA RD 08-0075Anomaly Detection and Multimodal Data in Astrodynamics13Space Catalog Anomalies

22,000 objects in the catalogWhat is an anomaly?New satellite is launchedDebris is createdSatellite maneuversObject driftsSatellite status has changed

Track 22K objectsLook for deviations

Paul SchumacherThe Future Space Catalog14Movie: Amosphere

AMOS Conference 20089/11/2008 UpdateGar Hassal14AFRL/PA RD 08-0075Multimodal Space Catalog:Radar vs. Optical

RA1Dec1RA2Dec2RA3Dec33 observations ( x y z vx vy vz )6 scalars 6 scalars

Range2Az2 Elev2Range1Az1 Elev1

2 observations ( x y z vx vy vz )6 scalars 6 scalars 2 radar observations3 optical observationsRadar and Optical Observations translated into 3-D spatial coordinates15Track initiation for radar data is different from track initiation for optical systems. Because each radar observation supplies complete position data at a known time, a candidate orbit can be formed from as few as 2 radar observations. This fact leads to quadratic computational complexity in the track initiation problem, since all combinations of observations must be considered 2 at a time (N choose 2.)

In the case of optical systems, all traditional methods of track initiation have used 3 (or more) observations to solve for the candidate orbit. In particular, astronomers have developed a wide variety of algorithms based on this idea. Inevitably, though, this approach leads to cubic computational complexity because we must consider all combinations of observations 3 at a time (N choose 3). Is it feasible to build a large catalog of space objects using these traditional methods? There are good reasons to think that we cannot do this in any practical way.

SAB Review FY11: TECHNOLOGYUPDATE: 2010 Sep 8 -- DISTRO CAFRL/Directed Energy Directorate 15Observation Association is HardFor one object, all observations are connectedSST16

????Space Surveillance TelescopeFor many objects, all observations are connectedSBSS = Space-Based Space Surveillance System (an SMC program)SST = Space Surveillance Telescope (a DARPA program)UCT = Un-Correlated Target, one that cannot be immediately associated with the catalog of known space objects

SBSS acceptance for operations was delayed mainly because of lack of ability to handle UCTs from this sensor. The track initiation used to handle SBSS unassociated data proved to be inadequate.

The SST program was basically a sensor program and did not address the problem of how to exploit the data for SSA. Another recent DARPA program, Ibex, became the mechanism for us to finally develop a new track initiation approach for that sensor.

At present, most of the unassociated data from the GEODSS telescopes is ignored. The main reason is that no track initiation method currently available to operations is able to produce sufficiently accurate initial orbits from the GEODSS data.

The track-initiation and UCT-processing software tools that are available are designed for use by human experts and do not allow the high throughput necessary to handle the data flow from the upgraded GEODSS cameras (Deep Stare) or from either of the other 2 sensors listed here. Another problem is that the human analysts are no longer being trained to the level of expertise required for this task. The only answer is a fully automatic UCT processing capability that can handle the data types from all optical space surveillance sensors.

SAB Review FY11: TECHNOLOGYUPDATE: 2010 Sep 8 -- DISTRO CAFRL/Directed Energy Directorate 16Multimodal Data in Speckle Imaging17

Speckle Imagingusing Short Exposure SequencesHigh resolution details are lost in long exposures through the atmosphere:However, detail is encoded in short exposure images:

Assume that target is constant over period of a few seconds. Then image reconstruction is possible:

18First image-atm turblence changing quickly , long exposure wipes out image because atm changing so quickly, short exposure freezes the atm, fine details still there but hidden behind the images.AFRL/RD TMR1/4/2013Air Force Research Laboratory Distribution Statement F 18Speckle ImagingMulti-Frame Blind Deconvolution (MFBD)

o(x)True object

Restoredobject

MFBD ProcessingMinimize this cost function withrespect to , , , :

Noisy and blurred images

Blurring functions

19Multimodal DataImproves Image ReconstructionEach wavelength experiences ~same optical path difference (OPD) due to atmospheric turbulenceWavefront phase is = OPD 2/

LongerwavelengthShorterwavelengthOPD in telescope pupilInfrared images define OPD, which in turn improves visible reconstructionBrandoch Calef, Wavelength Diversity20Multimodal Data in Non-Imaging21Spectrophotometry with BASS at AEOS

IR spectrophotometry in 3-13.5 mm rangePrinceton CCD camera & filter collect images simultaneously with IR spectra

222222Modeling Reflected & Emitted Radiation

Modeling space debris to match simultaneous IR and visible response

HAMR objectsHigh Area-to-Mass Ratio

Mark SkinnerFusing Visible and Thermal IR Signature Data for SSA2324Anomaly Detection in Non-Imaging