Transcript No Slide Title

Fusion for the Army
Knowledge Fusion Research Workshop
Dale A. Walsh
Principal Engineer
The MITRE Corporation
Fusion SME for Army DCS G-2
October 2004
(Version 2004.02)
Definition(s) of Fusion
• JCS Pub 1-02
The process of examining all sources of intelligence and information to derive a complete
assessment of an activity.
• Textbook
A process dealing with the association, correlation, and combination of data and information
from single and multiple sources to achieve refined position and identity estimates, and
complete and timely assessments of situations and threats, and their significance. The process is
characterized by continuous refinements of its estimates and assessments, and the evaluation of
the need for additional sources, or modification of the process itself, to achieve improved
results. (Llinas/Hall)
• Textbook, Simplified
A process of combining data or information to estimate or predict entity states.
(Steinberg/White)
• Army’s “Usable”
A series of processes performed to transform observational
data into more detailed and refined information,
knowledge, and understanding. (Walsh/Jones)
These processes, by their very nature, will involve both automated
processes and human cognition. Also included as part of fusion are the
databases, human interfaces and information portrayal, and the control
and feedback of the fusion process.
Fusion “Truisms”
• Fusion is not new; automating some of it is
• Fusion is a “critical enabler”
• Fusion is driven by the Commander’s
Priority Intelligence Requirements (PIRs)
• Fusion takes place simultaneously at all
echelons
• Fusion facilitates Actionable Intelligence
Definitions
• The JDL Fusion Model – A taxonomy and concept initially
developed in the 1980’s by the then-Joint Directors of (DoD)
Laboratories
– Designed to allow the diverse fusion efforts to have a common
language and framework – has worked very well to that
purpose
– Is NOT a system architecture, notional or otherwise
– Is NOT intended to define problem sets that clearly vary from
armed service to armed service
– The “boundaries” between fusion “levels” are not intended to
be completely distinct (so some processes may touch adjoining
levels)
– Is now shepherded by a panel of “graybeards” who still debate
and modify the model on occasion
The Data Fusion Model
(from Joint Directors of Laboratories, as modified)
Level
0
Fusion
Level
1
Fusion
Level
2
Fusion
Level
3
Fusion
Level
5
Fusion
Sources
&
Sensors
Level
4
Fusion
Databases
(Fusion & Support)
Human
Computer
Interaction
What do these levels do for me?
• Level 1 fusion tells you what is physically out
there
• Level 2 fusion tells you how they’re working
together and what they’re doing now
• Level 3 fusion tells you what it all means, what
will happen next, and how it affects your plans
• Level 4 fusion tells you what you need to do to
improve the information from levels 1-3
• Level 5 fusion allows you to look at and interact
with the information from levels 1-4
Entities vs. Fusion Levels 1-3
Entity Tiers
Tier 1 - Direct Observables
(equipment, facilities, physical infrastructure)
Tier 2 - Reportable Items
(lower echelon units, individuals, specific events)
Tier 3 - Conclude Existence
(upper echelon units, organizations, comms nets,
interrelationships between other entities)
Tier 4 - Post-analysis
(plans/courses of action, intentions, behavior at
both the enterprise and sub-enterprise level)
We have taken the types or classes
of entities and other knowledge
goals and categorized them into
“tiers”. On one end, Tier 1 entities
are ones that can (in general) be
observed directly; conversely, Tier 4
entities (really knowledge goals
more than entities) are items of
knowledge that can never be
directly observed but can only be
developed by cognitive analysis.
Entities vs. Fusion Levels 1-3
Entity Tiers
Tier 1 - Direct Observables
(equipment, facilities, physical infrastructure)
Tier 2 - Reportable Items
(lower echelon units, individuals, specific events)
Tier 3 - Conclude Existence
Battlefield entities, and further
knowledge and understanding about
them and their actions/intents, are
developed in Fusion Levels 1, 2,
and 3 only.
Levels 0 and 5 are simply pre- or
post-cursors to that goal, not
“lower” or “higher” levels of fusion.
(upper echelon units, organizations, comms nets,
interrelationships between other entities)
Tier 4 - Post-analysis
(plans/courses of action, intentions, behavior at
both the enterprise and sub-enterprise level)
Level 4 is the feedback loop that
uses what is learned in Levels 1-3 to
drive improvements in processing
(or collection tasking)
Entities vs. Fusion Levels 1-3
Entity Tiers
Tier 1 - Direct Observables
(equipment, facilities, physical infrastructure)
Tier 2 - Reportable Items
(lower echelon units, individuals, specific events)
Tier 3 - Conclude Existence
(upper echelon units, organizations, comms nets,
interrelationships between other entities)
Tier 4 - Post-analysis
(plans/courses of action, intentions, behavior at
both the enterprise and sub-enterprise level)
Level
1
Fusion
Knowledge is developed by
continuous cycling of current
knowledge and incoming data
through Levels 1, 2, and 3 and this cycle can occur at
each echelon.
Level
2
Fusion
Level
3
Fusion
Nouns --> Verbs --> Sentences --> Paragraphs
Current Fusion “State of the Art”
• Level 0 fusion handled by sensor communities
• Generating objects is not automated for all domains
• Level 1 fusion largely conquered - automated
correlation and update techniques in use for 10+ years
• Level 2 fusion is largely unautomated, yet current
and future quantities of observations call for some
levels of machine assistance
• Automated processes need “templates” for aggregation rules
• Level 3 fusion will remain the G2/G3 analysts’
domain for the near future
• Level 4 fusion is handled by the collection
management arena
• Level 5 fusion is a “visualization thing”; user
interaction for feedback/control is at various depths
How much Automation?
How much Cognition?
•
•
•
The desired or achievable balance between machine
and Soldier has led to much debate
An Army Working Group will be stood up to try and
address how much automation is “enough”
Four Modes of Operation have been defined:
1. Manual: No applications software to help
2. Computer Assisted: Tool Box of applications
3. Semi-Computer Controlled: Manned Assembly Line (series
of applications run under user supervision)
4. Total Computer Controlled: Automatic Assembly Line
Notional Fusion Informational Flow
Pixels, waveforms,
grunts/squeals, etc.
Sensing/
Reporting
Entities
Eqmt/Unit/Facility/
Organization/Individual/
Event
Hypothesized
Current Enemy
COA(s)
Current
Behavior
Assessment
(Level 2C)
SU
RE
Entity Extraction
Entities
w/tech data
Entities w/tech data
Entities
w/o tech data
Correlated
Events
Event/Activity
Aggregation/
Analysis
(Level 2B)
Entities
without
Domain-specific
Correlation (Level 1)
Entities
Generic
Correlation (Level 1)
Correlated
Objects
Tgt
COP SA
Object Aggregation/
Analysis (Level 2A)
Aggregated
Note: Decomposing
Level 2 into 2A/B/C is an Army-unique
objects
concept to permit more detailed analysis of the classic L2 notions
Intelligence Domains
All Source (Integrating) Domain
Human
Domain
Soldiers as
Sensors
CI/HUMINT
Open Source
Signatures
Domain
Signals
Domain
Imagery
Domain
Visible
MASINT
(exc. Imagery)
COMINT
Externals
COMINT
Internals
ELINT
Infrared
Radar
(incl. SAR, MTI)
Multi/Hyper
Spectral
Multi-INT (Intra-domain) Exchanges
An Analogy
(Thanks to Rex Williams, USAIC&FH DCD)
• The COP is like taking a snapshot of a soccer
game: it will show you where the players and the
ball are at that point in time, but it doesn’t show
the flow of the game
• The Intel Running Estimate (RE) is like a video of
a soccer game: it shows the flow of the game and
allows the viewer to project how they think the
game is going and will go
030440Z - Seismic detection
030440Z - Acoustic detection
030441Z - Tracked vehicle on move
030443Z - Acoustic detection
030444Z - Tracked vehicle westbound
030445Z - MTI reports 8 vehicles
030447Z - UAV Video shows
tanks on road
030448Z - Radio intercept of
3rd Tank BN
0445Z
03 Sep 0448Z
0450Z
0441Z
0444Z
0440Z
Looks
like8
the
42nd
Tank
BDE
istowards
going
toour
attack
There’s
3rd Tank
Elements
The
42nd
some
tanks
of
BN
Tank
the
vehicles
is moving
BDE
moving
42nd
ismoving
out
Tank
west...
moving
west
there...
BDE
west...
are moving
position
westour
something
out
there...
communications facility at Bradlehofer
Now, A Word from Our Sponsor
While the last slide showed a very realistic
operational example, fusion in today’s
Army cannot be constrained to the simple
problems of observing, analyzing, and
predicting movement of conventional units.
The tasks of fusing information regarding
non-linear battlespaces, terrorism cells, and
other 21st century matters must also be
added to fusion’s “plate”.
An Army Operational Extension to
the JDL Data Fusion Model
Level
5
Level
3
Level
1
Solid lines – primary data flow
Dashed lines – alternate data flow
– human interaction
– displays, reports, etc.
Level
2
Analysts
Level
4
Warfighter
Data
Repository
COP/RE,
BCS
Level
0
Sensors
Fusion from “Space to Mud”
Home Station
EAC (HSOCs, IDC)
CORPS
UE DCGS-A
DIV
BDE
FCS
with
BNUA DCGS-A
embedded
CO
software
PLT
OFW
SQD
Organic/
Nat’l
Sensing
Σ
Organic
Sensors
Σ
Organic
Sensors
Σ
Organic
Sensors
Σ
Tactical Overwatch Concept
• Overwatch is a new, advanced concept for providing
dedicated, focused intel to operationally engaged UA units
(BDE/BN)
• Overwatch “spotlights tactical forces with the full power of
the theater, joint, and national set of intel capabilities”
• Overwatch provides the UA high-level fusion along with
collection management and targeting support
• Overwatch provides sustained culturally-aligned global
awareness conducted daily (and operates in peacetime and
during war)
• Overwatch provides support to engaged UAs while
anticipating transitions and future operations
• MI Brigades (as UEy) are the platforms from which
Overwatch is executed, with INSCOM’s IDC acting as a
hub for all Overwatch activities
Part Two: Fusion Issues
Fusion for Dismounted Squads
• Fusion Goals
– Locally-focused Level 1 fusion on organic sensing
to produce the local COP/SA
– Integration (and resolution) of the SA/SU products
from FCS/UA above
– Semi-autonomous Level 4 asset management
– Soldier-supportive visualization/reporting
• Organic Sensing
– EO/IR, acoustic/magnetic/seismic, CBRN, soldiers
• Efforts
– Objective Force Warrior, Warriors’ Edge (ARL)
Fusion for Unit of Action
• Fusion Goals
– Regionally-focused Level 1 fusion on a huge multi-INT organic
sensing flow, leading to generation of COP/SA
– Integration and deconfliction of the SA from squads below and
the SA/SU of tactical overwatch support from above
– Effective cross-cuing to maximize utility of organic sensors
– Level 2/3 fusion to see the aggregated red and blue picture and
then develop predictions on how/where the focused conflict
might develop (ie, SU)
– Intelligent management of “steerable” collection assets
– Seamless use of fusion products between ISR and C2
• Organic Sensing
– EO/IR/SAR, video, LADAR, acoustic, seismic, magnetic, radars,
GMTI, CBRN, Met, EMTI
• Efforts
– FCS with DCGS-A embedded software, FBKFF STO
(ARL/I2WD, Level 2/3)
Fusion for Unit of Employment
• Fusion Goals
– Globally-focused Level 1 fusion within each discipline
feeding an all-source fusion process which also leverages
COP/SA/RE/SUs from below/above/joint
– Level 2/3 fusion to aggregate forces then predict future
developments, put them in context with
terrain/weather/plans, and develop knowledge about
intended behavior/driving forces and their impact
– Joint operations (true at all echelons, but critical here)
• Organic Sensing
– COMINT, ELINT, IMINT, MASINT, HUMINT, OSINT,
Soldiers
• Efforts
– DCGS-A, FBKFF STO, DARPA RAID (Level 3)
Fusion for Overwatch
• Fusion Goals
– Extract relevant information from the large “take” available
at the overwatch level
– Make that information usable and actionable
• Provide strategic-level SA/SU
• Provide tactical overwatch support (to echelons as low as UA) in a
timely, adjudicatable form
– Provide models, guidance, context information, etc. to
support fusion processing at all echelons
• Sensing
– Vast amounts of strategic/national intel
– Some organic sources
• Efforts
– INSCOM’s IDC work and research, NGIC
Fusion “Longpoles” (Page 1)
• ATR/AiTR - The quantity of imagery and video is exploding, yet
the ability to extract objects and information from those feeds in
an automated way trails badly. Techniques like change detection
are available but not implemented on a large scale.
• Cross Domain - Intelligence domains breed classification
domains, which restrict the timely flow of critical intelligence to
those who need it. The resolution of this issue is as much
political/procedural as it is technical. Intelligent agents can work
to provide critical information while still protecting sources and
methods - if their implementations are approved for use.
• Sensor Webs - A simple web of 20 deployable MASINT sensors
can produce an overwhelming amount of raw data - so much that
it becomes critical for Level 1 fusion first be accomplished
within the web. This fusion must still produce a product that can
be efficiently fused with other info/product held upstream.
Fusion “Longpoles” (Page 2)
• Text/Speech Translation - Translation of printed, handwritten, and
spoken languages is needed to begin to exploit the vast amounts of
available non-formatted information sources. Translation support
must be able to have a small footprint at SQD (for limited
capabilities) and support high volumes for overwatch.
• Text/Speech Exploitation - More robust capabilities are needed to
first parse (given post-translation grammatical constructs of each
language) and then to extract/make sense of “free text” data.
Eventually the ability to infer meaning into jargon, etc. needs to be
explored.
• Scalability of Level 1 - While ASAS and TES provide very viable
(and yet different) Level 1 correlation capabilities today, the sensing
capabilities of an FCS UA will increase the input stream 100-fold.
The scalability of current approaches/software has not been
determined.
Fusion “Longpoles” (Page 3)
• Distributed Level 1 Architecture, with Tactical Overwatch Given the required analytical timelines and the realities of future
communications throughputs, Level 1 fusion cannot be centralized
for a UE or even a UA. Distributing fusion within a UA implies
that issues of adjudication between varying non-synchronized
views must be dealt with, and only in a cohesive metadata/data
tagging environment. This problem has not be solved nor even
sufficiently researched to date. The ability to effectively cross-cue
from both single domain and all source results will also be critical.
And the addition of tactical overwatch products into the fusion at a
UA will complicate a distributed fusion architecture even more.
• Reconstruction of Functional Networks - The application of
varied techniques to sort through quantities of information and
discover/confirm/assess the non-obvious, n-deep interrelationship
networks is not widespread, nor is it readily manageable by average
analysts. This capability is critical for many current operations.
Fusion “Longpoles” (Page 4)
• Level 2 Fusion at Tactical Echelons - Given insufficient staffs at UA
echelons, the need to perform Level 2 fusion analysis with sufficient
automated support on the constrained problem view of the tactical
commander is critical.
• Level 2 Fusion at Overwatch Echelons - At higher echelons, the ability
to constrain the problem view must be lifted. More robust Level 2
capabilities, especially for urban, asymmetric, stabilization, and other
types of operations, will need to be developed.
• Red Models - In order to support Level 2 fusion at both tactical and
overwatch echelons, a significant amount of prerequisite knowledge of
enemy doctrine/beliefs/structure/methods/etc. will need to be first
developed and then formulated for use by automated fusion processes.
• Simplified User Interfaces - Tactical users see/sense much information,
yet have no simple, quick way of inputting that information into the
fusion process. Similarly, they need easy, understandable access to the
results and products of fusion.
Mapping “Longpoles” to Echelons
Longpole
Dsmtd SQD
ATR/AiTR

Cross Domain
SBU
Sensor Webs
Text/Speech Translate
Text/Speech
Exploitation
Distributed L1 Arch/
Tactical Overwatch
Scalability of Level 1




Reconstruction of
Functional Networks
Level 2 - Tactical
Level 2 - Overwatch

Red Models
Simplified User I/F

Unit of Action

Unit of Empl
Overwatch

SCI
Coll
plus










Use



Use







Build
Part Three: Level 1 Fusion
Concepts and Architecture
Level 1 Fusion Flowchart
HCI
Knowledge Bases
Reports/
Observations
Alignment
Correlation
Spatial
Alignment
Candidate
Retrieval
Temporal
Alignment
Candidate
Scoring
Identity
Alignment
Candidate
Assessment
Technical
Alignment(s)
New, add
Already seen
Correlated
Entities
Combination
(State Estimation)
Determination
of “best”
Resultant
record
•COP
•Targets/BDA
•Sit Alarms
•Level 2 Processes
The Advantage of Multi-INT
(a notional example, not anyone’s official chart of sensor capabilities!)
Type of
|Entity Type | Process|
INT
|U Q F I E O | Speed
COMINT Ext
COMINT Int
x
fast
x x x x x x
ELINT
x
x
* *
x
HUMINT EPW
slow
fast
IMINT EO/IR x x
IMINT MTI
med
fast
x x x x x x
slow
MASINT ASM
x
x
fast
MASINT MS/HS
x x
x
slow
OSINT
| Loc
x x x x x x
slow
ZSU-23-4, seen by
EO and MTI
med
EO and MTI and ELINT
med
EO and MTI and ELINT
and COMINT Int
slow
Classes of Knowledge
Mvmt
Time
Type
ID
Comp
|
Status
Intent|
Part Four: Level 2 Fusion
Concepts and Architecture
(with thoughts on Level 3)
(Version 2)
(still a work in progress)
Level 2/3 Fusion at UA
170,000 reports => 105 tracks
105 tracks => 15 “units”
15 “units” => a BN-sized force
moving to contact this way
threatening our forces here
Scenario taken from USAIC&FH MAPEX
• Level 1 fusion
correlates inputs and
produces a set of tracks
• Level 2 first associates
and aggregates those
tracks into forces
• and those forces into
force structures
• then proposes that the
force is exerting (L2)
/will exert (L3) itself in
some way
• which will threaten our
forces and require the
battle to be “shaped”
Why Level 1 Fusion Isn’t Enough
UE
Bde COP
Sensor Acquired Data
Fused to Level 1
Plus…Information from
echelons above UA
Bn COP
170K+ Reports/Hour
If only Level 1 fusion is
done, there is no roll-upC of 56K+ Reports/Hour
om
entities, and the Bde COP
pl
carries all the “little things”. e x i
Report
Reportcount
countbased
basedon
on
DCGS-A
MAPEX
DCGS-A MAPEX
results
resultsusing
usingCaspian
Caspian
Sea
Scenario
Sea Scenario
Reports generated from FCS EO/IR and COMINT Sensors only.
Add MASINT sensors and reporting at UA goes to @ 600K/hour.
With only L1: 500+ icons
With L1 and L2: >50 icons
ty
Co COP
18K+ Reports/Hour
PLT COP
6K+
Reports/Hour
Level 2/3 Fusion State-of-the-art
Level 2 - Situation Refinement
JDL Fusion model proposes
(how they work together
concept of Levels 2 and 3
and what they’re doing)
Level 3 - Threat Refinement
Mid 1990’s
ASAS makes small inroads to
(what it means and how it
automating some Level 2 fusion
affects our plans)
Mid 1980’s
2000-2001
FCS/OF concept emerges - trades armor
for ISR/Knowledge (aka fusion)
Dec 2001
DARPA Level 2/3 Symposium - sets
an 8-year path for research
(RAID planned for FY05 start)
Jan 2002
Level
0
Fusion
Level
1
Fusion
Level
2
Fusion
Level
3
Fusion
G-2 says 8 years too long;
develop ‘03 STO
June 2002
Level
5
Fusion
Sources
&
Sensors
Level
4
Fusion
Human
Computer
Interaction
Databases
(Fusion & Support)
Joint I2WD/ARL
FBKFF STO approved
Notional L2/3
Architecture
presented
Oct 2002
Level 2 Fusion Flowchart
(a work in progress)
HCI
Correlated Simple
Objects
Correlated Complex
Objects
Object
Aggregation
“Linked”
Correlated Entities
Aggregated,
linked set
of objects
Aggregate
Analysis
Knowledge Bases
Event Reporting
Deception
Assessment
Red Models Learning
Context
Interpretation
Environment
Event
Aggregation
Hypothesis
Assessment
“Linked”
Events/Activities
Hypothesis
Generation
Behavior Hypotheses
(large and small)
Event
Derivation
Event
Interpretation
& Analysis
Hypotheses of the current behavior/
motive/objectives of the enterprise and its
constituents, with some sense of
validity/score of each viable hypothesis
Level 2, Decomposed
“Level 2A” – Object Aggregation/Analysis
Correlated Simple
Objects
Correlated Complex
Objects
Object
Aggregation
“Linked”
Correlated Entities
Aggregated,
linked set
of objects
Aggregate
Analysis
Knowledge Bases
Event Reporting
“Level
Deception
Assessment
Red Models Learning
Context
2C”Interpretation
– Current
Environment
Event
Aggregation
Hypothesis
Assessment
Behavior Assessment
Event
Derivation
“Linked”
Events/Activities
Hypothesis
Generation
Behavior Hypotheses
(large and small)
“Level
2B” Event/
Activity
HCI
Aggregation/
Analysis
Event
Interpretation
& Analysis
Hypotheses of the current behavior/
motive/objectives of the enterprise and its
constituents, with some sense of
validity/score of each viable hypothesis
Some Object Aggregation Techniques
• Template-based
• Example: Observations of lower-tier units or equipment spawn
inferred units of one higher echelon
• Traffic Analysis
• Example: Piecing together hundreds of observations of emissions to
reconstruct a communications network
• Geolocation/Movement
• Examples: a unit that stays at a facility may be using the facility; a
unit whose equipment is moving west is likely also moving west
• Other Data Mining
• Example: What n-deep relationships can two people have if they talk
to the same people or share the same religious center?
Activity “Trees” of Events
“3 Events”
Tank #3713
Tank #3717
moving
N#3719
Tank
moving N
moving N
+ association
of these three
tanks to the
33 TK CO
Event: one object
performing one
simple* action
(* be at, move,
shoot, emit, etc.)
Activity “Trees” of Events
Tank #3713
Tank #3717
moving
N#3719
Tank
moving N
moving N
+ association
of these three
tanks to the
33 TK CO
“Event Cluster”
33 TK CO
moving
North
(Conf 85%)
ADA at
Bridge #36
(Conf 90%)
Comms between
33 TK CO
and ADA unit
(Conf 95%)
Event Cluster:
complex object
performing
one action
Activity “Trees” of Events
“Activity”
Tank #3713
Tank #3717
moving
N#3719
Tank
moving N
moving N
+ association
of these three
tanks to the
33 TK CO
33 TK CO
moving
North
(Conf 85%)
Activity: upper ‘tree’
ADA at
construct built Bridge
from #36
(Conf 90%)
multiple objects
performing a complex
Comms between
33 TK CO
action (derived from
and ADA unit
a set of related
(Conf 95%)
simple actions)
ACTIVITY
MODEL #121 –
River Crossing
Unit moving
towards river
√
Bridge on unit’s
probable path
√
33 TK CO
to conduct
river
crossing
operation
Bridge #36
approx.
1345Z
(Conf 73%)
ADA deployed
at/near bridge
√
Level 2 Fusion Flowchart
(a work in progress)
HCI
Correlated Simple
Objects
Correlated Complex
Objects
Object
Aggregation
“Linked”
Correlated Entities
Aggregated,
linked set
of objects
Aggregate
Analysis
Knowledge Bases
Event Reporting
Deception
Assessment
Red Models Learning
Context
Interpretation
Environment
Event
Aggregation
Hypothesis
Assessment
“Linked”
Events/Activities
Hypothesis
Generation
Behavior Hypotheses
(large and small)
Event
Derivation
Event
Interpretation
& Analysis
Hypotheses of the current behavior/
motive/objectives of the enterprise and its
constituents, with some sense of
validity/score of each viable hypothesis
Critical point:
As is true between Levels 1, 2, and 3,
the interaction between Levels 2A, 2B,
and 2C are not linear nor necessarily
sequential.
Knowledge is developed by a continual
and ad hoc application of all these levels
of fusion processes.
Level 3 Fusion
Takes the Course of Action hypotheses from
Level 2C, vetted by the evidence from Levels 1,
2A, and 2B, and projects future Courses of
Action hypotheses and assesses them vis-à-vis
the capabilities, vulnerabilities, opportunities,
environment, etc. of both red and blue.
(A notional architecture remains to be tackled.)
Part Five: Fusion Developments
Topics
Enabling Technologies
• Ontologies provide organization of the terms and concepts, to
permit clearer cross-understanding of what things are
• Intelligent Agents are software that can, given a set of
actions/reactions, provide automation to tasks such as smart
dissemination, alert/alarms, sanitization/declassification, etc.
• Knowledge Bases/Management are advanced IT techniques for
capturing and manipulating the “higher level” abstractions of
information (environment, belief, intent, etc.)
• Service-based Architectures are the emerging means to support
net-centric operations; they imply that the processes will be able
to function as invokable services hosted within some network
space
• Distributed Fusion Architectures are approaches to implementing
fusion processing across a multi-echelon net of fusion process
services without the creation and use of a central fusion node
(to date, this is a very immature field of study)
Army Goals for Level 2 Fusion
• “Unit of Action” Level 2 goals
Given that we can sense where the threat is...
– Where is it going and what are its near-term goals?
– When will they see us and decide to shoot?
• “Unit of Employment” Level 2 goals
– Who are the players and where are they operating?
– What are the large-scale objectives?
– Why are they doing what they are doing?
BOTTOM LINE: We cannot hope to completely automate these goals…
but given the vast amounts of observations that will be collected, we need to
find ways to automate hypothesizing knowledge from amidst the information
Part Six: Current Army-Oriented
Fusion Work
Army Fusion Efforts
• Program Development
•
•
•
•
Objective Force Warrior
Future Combat System
DCGS – Army (incl. ASAS, TES, CHIMS, CGS)
INSCOM/Information Dominance Center
• Research
• Warrior’s Edge/Horizontal Fusion Initiative
• Warrior’s Lens – ARL 6.1 Fusion Work Group
• SIBRs
» Adv Viz Support to Fusion; Ontology-based Fusion Model; VMTI Tracker
• STOs/ATDs/ACTDs
» FBKFF, Eye in the Sky, Netted Sensors, BTRA, Gnd Station Tech Testbed
• Several smaller studies on specific topics
» Urban issues, MASINT phenomenologies, etc.
» AIS/Univ Buffalo IR&D on Graph Theory
• Cooperation across DoD
• DoD-level fusion community symposia and coordination
• Leverage of DARPA efforts (RAID, DDB, DTT, AIM, etc.)
Part Last: Conclusion
A Concluding Thought:
Twenty years ago, the automated fusion of observations and reports
into a correlated set of battlefield objects was a worthy but
unexplored concept - and research was begun.
Ten years ago, every service had fielded systems that automated
some aspects of Level 1 fusion. The fidelity of those systems
continues to improve as time goes on.
Army leadership is now preparing to trade some of its heavy
armored vehicles for lighter and faster vehicles. But this will only
be effective if we can produce quick, predictive views of the
battlespace to protect and empower that fighting force. To do this,
we must advance the state-of-the-art of fusion, especially beyond the
Level 1 correlation techniques done today.
And if we want to advance that state-of-the-art in time for that
exchange of armor for knowledge, we need to aggressively begin the
work now.