Insight National Academies Board on Global Science and Technology Committee on Integrating Humans, Machines and Networks: A Global Review of Data-to-Decision Technologies Washington, D.C. February 26, 2013 Ben Cutler Program Manager Distribution Statement A Approved for Public Release, Distribution Unlimited
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Insight
National Academies Board on Global Science and Technology
Committee on Integrating Humans, Machines and Networks:
A Global Review of Data-to-Decision Technologies
Washington, D.C.
February 26, 2013
Ben Cutler
Program Manager
Distribution Statement A Approved for Public Release, Distribution Unlimited
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• Combine data across multiple sources
• Manage efficient use of sensors and platforms
• Identify threats using behavioral discovery and prediction algorithms
• Collaborate
Insight: one unified global ISR picture
An adaptable, integrated human-machine ISR exploitation system
Distribution Statement A Approved for Public Release, Distribution Unlimited
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• Data from diverse sources
• Ambiguity/uncertainty of information
• Who? What? Where? When? Why?
• Pedigree, provenance
• Report of an observation (delivery of electrons) may be received long after the event
• Sequence neutrality – obtain the same results regardless of the order in which data is integrated
• Tactical relevance requires real-time insights
• Real-time analysis
• Real-time data sharing
• Data analytics at massive scale
• High computation requirement per unit data
• Fleeting existence of a critical piece of information buried in a sea of data
• “Bad guys hiding in the noise floor”
Challenges
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• Threat networks include the following elements
• Movement
• Command and Control
• Intelligence (includes collection and deception)
• Logistics
• Fires (IEDs, artillery, air defenses, aircraft)
• Protection (physical defenses, air defenses, electronic systems)
• Identification by
• Signatures
• Physical characteristics indicate this aircraft is a fighter jet
• Activity Based Intelligence
• Pattern of life (e.g., activities, associations) indicate a group of people appears to be an insurgent cell
Finding threat networks
Distribution Statement A Approved for Public Release, Distribution Unlimited
Vast sea of data
Find the threat network (all names fictional)
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• Correlate unusual activity with historical information and intelligence estimates
• Determine past and current locations of people of interest, vehicles, and other artifacts, and estimate patterns of life
• Detect deviations from normalcy that warrant further analysis
• Set up tripwires or watch boxes that alert when an event partially or fully matches a pattern of interest based on some combination of space, time, and network relationships
• Alert the analyst to any meetings in the south end of town
• Task assets to fulfill information requirements
• And alert the analyst when an information requirement (information that is needed for understanding or for making timely decisions) is fulfilled
• Situational analysis: what has happened, what is happening, what is likely to happen?
Some system requirements for finding insurgent networks
Distribution Statement A Approved for Public Release, Distribution Unlimited
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Data to information: VIRAT interpretation of video
Activity types Digging Carrying Walking
Video data collected from Creech AFB, March 2009
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Data to information: wide area motion imagery example
• 25 cm GSD
• Six cameras with ortho-rectified (stitched and geo-
registered) imagery
• NITF file format
with encoded sensor metadata
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AFRL RYA LAIR Dataset, 21 October 2009 WAMI Data
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Tracking result
Wide area
motion imagery example
Tracking result
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Relevant information content (ground truth)
~6,500 tracks in 7 minutes
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• Raw sensor data
• ~200 megapixels/second
• Pixel format and interpretation is sensor-specific
• Generally, we just want information about the entities in the video
• Where are the cars and people?
• Features (e.g., large, red sedan)
• Activities – what are they doing? (e.g., car making a u-turn)
• Information: “tracks”, one per active entity
• In this example: 200 megapixels/second => 15 tracks/second
• Tracks distill data into computable information that Insight can process
• Track format accepted by Insight is standard across motion imagery sensors
• Model-based presentation allows downstream algorithms to be sensor-agnostic
• Track information may include feature or activity information
• Very large wide area motion imagery sensors
• 10s of gigapixels/second
• 10+ kilotracks/second
Data to information: wide area motion imagery
Distribution Statement A Approved for Public Release, Distribution Unlimited
• Real-time information integration and automated exploitation
• Multi-echelon, integrated, theater through tactical unit level support
• Sensor ISR: space, air, and ground
• Other information sources: soldier information, reports, CI/HUMINT, SIGACTs, IPB,
demographics, friendly locations (BFT), OSINT, law enforcement, commercial sources, etc.
Diverse information sources
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• Automated resource
management for
dynamic tasking and
cross-cueing based on
intelligence
requirements
• Direct support
systems
• Organic systems
• General support
systems
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Operational concept
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Insight World Model
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Object types provided by Intel Object types created by reasoning components
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Data conditioning
• Physical sensors have unique
reference frames – space/time bias
• Observer location, perspective
• Observed entity locations
• Incomplete data
• Obscuration
• Omission (e.g., HUMINT uncertainty)
• Duplicate data
• Multiple tracks or detections for a single entity
• Conflicting elements of a textual report
• Conditioning
• Provides a common reference frame
• Estimates incomplete or conflicting data
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Performance example - Blue Devil HDBlue Devil video frame overlay onto PeARL foundational image map
Using raw sensor metadata
Example - Blue Devil HDBlue Devil video frame overlay onto PeARL foundational reference map
Unregistered - using raw sensor metadata
PeARLReference
Blue Devil Unregistered
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Blue Devil Registered
PeARLReference
Example - Blue Devil HDBlue Devil video frame overlay onto PeARL foundational reference map