Transcript 22 ISMOR INCIDER brief V1.0

A new technique to address
CID and IFF studies
David Dean, Kathryn Hynd, Beejal Mistry, Alasdair
Vincent and Paul Syms
Dstl IMD and LSD
22 ISMOR, September 2005
Dstl/CP16723
Contents
• Introduction and definitions
– CID project background
– the technical problem
• Outline of the INCIDER model
– decision engines
– validation
• Initial successes?
• Questions
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Introduction: What is amicide?
• Definition of amicide (fratricide, friendly fire …):
– “An attack by one or more initiators acting as a group on one or
more friendly targets that are under friendly control”
• Includes attacks that result in no casualties or damage
– these are excluded in the US definition
• A ‘near-miss’ is when firers nearly attack friends
– but the error is realised before a shot is fired
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Causes of amicides
• Causes of 1167 20th. century events analysed by Dstl:
45%
40%
% incidents attributable
35%
30%
25%
20%
15%
10%
5%
0%
% C2
% Com
% Nav
% IFF
% ID
% Mvt
% Wpn
% Del
% unkn
Causes
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Consequences of amicide
• Casualties to Blue forces
– estimated at 10–20% of all casualties in WW1 and WW2
– greater in proportion if enemy less effective
• Reduces tempo
– including effects of lost opportunities to engage
• Impact on morale
• Political implications
– nationally and within coalitions
– used to undermine confidence in military
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Introduction: What is CID?
• UK definition of combat identification (CID):
– “The process of combining situational awareness, target
identification, specific tactics, techniques and procedures to
increase operational effectiveness of weapon systems and reduce
the incidence of casualties caused by friendly fire”
• Thus there are 3 methods of improving CID:
– situational awareness (SA)
– physical target identification (TID)
– tactics, techniques and procedures (TTPs)
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The CID OA problem
• MoD requires advice on CID cost-effective CID solutions
– BoI across SA, TID and TTPs
– across all environments – sea, land, air … joint and combined
– spans the physical, information and cognitive domains
• Cost-effectiveness implies quantitative modelling
– cognitive domain usually addressed using ‘soft OA’ methods
• No quantitative ‘off-the-shelf’ assessment tools available
• Dstl understood all domains to a sufficient extent …
– and was aware sufficient data existed to support modelling …
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The INCIDER model
What is INCIDER?
Integrates human,
physical and
operational domains
A repository of parameters that
will impact upon CID, and the
relationships between them
• The Integrative Combat Identification Entity Relationship
Model
– a generic representation of combat entities observing and
identifying prior to engagement
– can be tailored to represent all potential encounters where CID
is a contributory factor
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INCIDER conceptual overview
Operational domain
Physical domain
• Organic sensor characteristics
• Target characteristics
• Environment – e.g. terrain, weather
Human domain
• Pre-set characteristics
• Variables, e.g. from training
• Expectation, e.g. from briefings
• Motivation
• Physiology – e.g. stress and fatigue
• Scenario complexity
• Context and RoEs
• Possible target options
INCIDER output:
P(correct ID) and
time to ID
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CID decision-making scope
Memory
Decision process
Perception
Comprehension
Projection
Decision
Absolute
truth
about
identity
Retrieved
information,
reports
Compiled
view
Fusion
process
Decision output categories
Detection
Is it a military
object?
Identification
What sort of
tank is it?
Recognition
Is it a tank?
Action
Should I kill it,
report it, hide
from it or
ignore it?
Aggregated
information
available to
observer
Total
information
available for
decision
“Picture”
Organic and
3rd. party
information
Maximum
information
available to
observer
Battlespace
Entity
Maximum
information
available to
sensors
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Stages in a typical encounter
• Pre-conceptions
– from plans, briefings and attitudes
• Initial contact
– target might be Red, Blue, White or a non-target
• Build up confidence
– by seeking additional information
• Classify and decide
– take action (outside current model)
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Preconceptions
60% Enemy
20% Neutral
20% Friend
75% Enemy
20% Neutral
5% Friend
100% Friend
Zone of Certainty
90% Friend
90% Enemy
5% Neutral
10% Neutral
5% Enemy
75 %Enemy
100% Friend
20% Neutral
100% Neutral
5% Friend
90 % Classification Range
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90 % Detection Range
UK UNCLASSIFIED
50% Neutral
10% Friend
40% Enemy
Dstl is part of the
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Initial Contact
Something there, I think
it’s hostile
Combined SA and
positional Errors
100% Neutral
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Stale Friendly
Position Report
2 September 2005
Maximum
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Range
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Build up confidence
Advance
Seek
information
from SA, EO,
BTID etc.
Contact HQ
Check
location
Check SA
Pause
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Send
in a scout
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Cognitive engines
• Fusion engine tracks likely target identity
– uses the Dempster-Shafer method
– similar to Bayesian inference, but using ‘confidence masses’
– starts with target ID pre-conceptions
– updated as new information received
• Decision engine has two functions:
– decides on further action before CID decision reached
– decides on target identity when confidence threshold reached
• INCIDER iterates loop until a CID decision is made
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INCIDER decision model overview
Pre-set human
parameters of
decision maker
DempsterShafer ‘fusion
engine’
Situation
awareness model
Battlespace
target object
Sensor model
Variable human
parameters of
decision maker
Confidence in
target identity
Iteration
during run
Decision
outcome
Decision
engine
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Classification
outcome
Expectation/
history
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Task
selection
Output:
ID: X at
time t
Dstl is part of the
Ministry of Defence
Way ahead for INCIDER
• Validation using synthetic environment
– with psychometric testing of participants
– collaborating with QinetiQ CHS and Land Division
• Better modelling of possible errors
– in physical, informational and cognitive domains
• Aim to embed INCIDER in combat simulation
– possibly Dstl’s Close Action Environment (CAEn)
– will improve context, but may encounter interface problems
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Validation: ‘model–test–model’
Validate
SE
modelling
Questions?
Validates
Vignettes
Human
factors data
INCIDER
model
Behaviour
Generate
s
Live
exercises
Constructive
simulation
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Initial impressions and
summary
Initial results
• Results were intuitively ‘sensible’
– sensitive to different scenario vignettes
– sensitive to physical, informational and cognitive factors
– sensitive to interventions in SA, TID and TTPs
• Different CID interventions helped in different vignettes
– sometimes ‘binary’, other times more subtle influences
• Interactions between some CID interventions seen
– e.g. training and provision of specific TID equipments
– statistically significant using ANOVAR
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Ministry of Defence
Initial successes
• Quantitative assessment across three domains
– enabling equitable comparison of different LoDs
– contributes to understanding human factors in warfare
• High levels of cross-disciplinary collaboration
– technologists and engineers
– military SMEs
– psychologists
– mathematicians … and to include cost forecasters
– brought together by operational analysts
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Ministry of Defence
Summary
• Dstl required to assess CID interventions quantitatively
– sensitive to parameters in SA, TID and TTPs
– sensitive to physical, informational and cognitive factors
• Built the INCIDER model
– and managed to provide acceptable ‘first cut’ data set
• Substantial success from first results
– sensitive to changes in scenarios and CID parameters
• Contributes to understanding human factors in warfare
– potential for application to other ‘fusion’ problems
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Questions?
Always keen to hear of amicide events for catalogue – compilation of
V2.0 is ongoing
– please e-mail me on [email protected]
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Dstl is part of the
Ministry of Defence