Situations for Situation Awareness
Dr. Dale A. Lambert
Information Technology Division
Defence Science and Technology Organisation
Salisbury, South Australia
[email protected]
COP
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SA ∩ COP
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what we provide
Integration
Endsley [1] defines Situation Awareness (SA) as follows.
Situation awareness is the perception of the elements in
the environment within a volume of time and space, the
comprehension of their meaning, and the projection of
their status in the near future.
As the perception, comprehension and projection
components of SA characterise mental attributes, SA is
understood as a mental phenomenon, and in the absence of
anthropomorphism, is understood to be about human minds.
So viewed, SA is not a computer system or a screen display
- it is a state of human awareness. Equation (1) succinctly
characterises its composition.
SA = Perception ∪ Comprehension ∪ Projection (1)
Psychology
1 Introduction
Equations (1) and (2) allow us to coarsely assess how
successfully a given set of COP processes meet our SA
needs. The SA we want should be delivered by the COP
information provided. Ideally, SA = COP. The portion of
SA that we actually obtain from the COP is SA ∩ COP,
which by (1), (2) and the distributive law, delivers the 9
matrix elements of figure 1.
Technology
Keywords: Situation awareness, data fusion, common
operating picture, situation assessment, ontology.
and integration processes (including telephones and
computer interfaces). Equation (2) succinctly characterises
the COP in terms of information deriving from
psychological, technological and integration processes.
COP = Psychology ∪ Technology ∪ Integration (2)
what we want
Abstract - This paper explores the relationship between
Situation Awareness, Common Operating Pictures and
Data Fusion to expose the need for Situation and Impact
Assessments. A framework for Situation Assessments is
then proposed and illustrated through reference to footage
from the movie, “The Hunt for Red October”
Perception
Comprehension
Projection
Technologies and technological aids are often introduced to
enhance the state of human awareness, and so the
advancement of SA is partly about psychology, partly about
technology, and partly about the integration of the two. In
that context, the concept of a Common Operating Picture
(COP) is often promoted as a vehicle for facilitating SA.
The United Kingdom Ministry of Defence has recently
provided the following broad notion of the COP [2].
The Joint Operational Picture [Common Operating
Picture] is the total set of information, in whatever form,
which is a managed and validated view of the history,
current situation and future plans for all components of an
operation.
As the COP exists to create SA, and the creation of SA is
partly about psychology, technology and the integration of
the two, it is possible to characterise the COP in terms of
information deriving from: psychological processes (for
example direct observation and cultural interpretation);
technological processes (such as sensors and databases);
what we provide
Figure 1 : Current COP contributions to SA
The currently practised conceptualisation of the COP tends
to:
• view “Picture” as some form of “dots on maps”
integration display;
• promote “Operating” as involving fusion technology
for joining dots to recognise objects; and
• seek psychological unity by regarding “Common” as
the dissemination of hierarchical fusion products to
all of the distributed contributing elements.
But that conceptualisation focuses only on the perception of
objects of interest, and even then, primarily on objects
identified through surveillance assets. Figure 1 illustrates.
The current conceptualisation of the COP therefore fares
poorly against our SA requirements. It totally fails to
consider the comprehension and projection aspects of SA
and only partially addresses the issue of perception.
2 Data Fusion
2.1 The JDL Model of Data Fusion
Lambert [3] defines data fusion as the process of utilising
one or more data sources over time to assemble a
representation of aspects of interest in an environment. The
Joint Directors of Laboratories (JDL) model is currently the
most widely accepted model of the data fusion process [4].
Figure 2 illustrates the current JDL model.
DATA FUSION
DOMAIN
National
Distributed
Local
INTEL
EW
SONAR
RADAR
.
.
.
Data
bases
SOURCES
Level 0
Processing
SUB-OBJECT
ASSESSMENT
Level 1
Processing
OBJECT
ASSESSMENT
Level 4
Processing
PROCESS
REFINEMENT
Level 2
Processing
SITUATION
ASSESSMENT
Level 3
Processing
IMPACT
ASSESSMENT
Human/
Computer
Interface
Database Management
System
Support
Database
Fusion
Database
Figure 2 : Current COP contributions to SA
Within this paper, attention is directed toward “level 1”,
“level 2” and “level 3” of the JDL model, by including “level
0” within “level 1”, and by absorbing “level 4” within each
of the other levels. Lambert [3] provides the following
revision of the Steinberg et.al. [4] definitions of these three
levels.
• Object fusion is the process of utilising one or more
data sources over time to assemble a representation of
objects of interest in an environment. An object
assessment is a stored representation of objects obtained
through object fusion.
• Situation fusion is the process of utilising one or more
data sources over time to assemble a representation of
relations of interest between objects of interest in an
environment. A situation assessment is a stored
representation of relations between objects obtained
through situation fusion.
• Impact fusion is the process of utilising one or more
data sources over time to assemble a representation of
effects of situations in an environment, relative to our
intentions. An impact assessment is a stored
representation of effects of situations obtained through
impact fusion.
Equation (3) succinctly identifies the product of the data
fusion process DF with its component object, situation and
impact assessment outcomes.
DF ≈ Object Assess ∪ Situation Assess ∪ Impact Assess
(3).
2.2 Data Fusion and Situation Awareness
A comparison between equations (1) and (3) is instructive.
• Perception is about “… the perception of the elements
in the environment within a volume of time and space
…”, while Object Assess involves a stored
representation of objects. If “stored representation”
means mental representation, then Perception ≈ Object
Assess.
• Comprehension is about “… the comprehension of
their meaning …”, while Situation Assess is a stored
representation of relations between objects. Attempts to
understand meaning are many and varied, but generally
conceptualise meaning in terms of either reference to
the world (e.g. [5]), language (e.g. [6]), propositions
(e.g. [7]), possible worlds (e.g. [8]) or psychology (e.g.
[9]). A common thread across all of these approaches is
that they involve relations between objects. If “stored
representation” means mental representation, then
Comprehension ≈ Situation Assess.
• Projection is about “… the projection of their status in
the near future” while Impact Assess involves a stored
representation of the effects of situations. These are
again closely aligned, and so if “stored representation”
means mental representation, then Projection ≈ Impact
Assess.
From these observations it is reasonable to conclude
Perception ∪ Comprehension ∪ Projection ≈
Object Assess ∪ Situation Assess ∪ Impact Assess,
and therefore
SA ≈ DF (4)
by (1), (3) and the limited transitivity of ≈. From (4) it
follows that situation awareness is the product of mental
data fusion!
If we instead confine data fusion to mean machine data
fusion, and so constrain “stored representation” to mean
machine representation, then
(Perception ∩ Technology) ≈ Object Assess;
(Comprehension ∩ Technology) ≈ Situation Assess;
(Projection ∩ Technology) ≈ Impact Assess.
From this we conclude
(SA ∩ Technology) ≈ DF (5).
Machine data fusion therefore delivers a technological basis
for situation awareness, while situation and impact
assessments respectively provide the technological
comprehension and projection aspects of SA otherwise
absent within the current conceptualisation of the COP.
Machine data fusion still requires integration with
psychology before we can secure SA. Lambert [10] outlines
how this might be done.
Though the JDL model provides the technological basis for
SA, its account of the conceptual, mathematical and
computational foundations of data fusion remains
surprisingly scant. Illustrative examples of the higher levels
of data fusion are also scarce. In response, the remainder of
this paper focuses on situation assessments. It promotes
situations as a conceptual and theoretical construct for
situation assessments and illustrates elementary use of
situations through an example.
3 Situations
3.1 Objects for Object Assessments
Object fusion exists because in our interaction with the
world, we are inclined to associate bundles of nearcoincident observable properties with objects, and to
associate the persistence of those objects with the observed
existence of those properties under periodic review. Object
assessments allegedly document persistent objects having
properties, and in a machine fusion context, these properties
are usually measurable. The “level 1” fusion literature
therefore tends to be numerically based. In a radar
environment, for example, signal and track processing is
often used to conclude the existence of objects associated
with, inter alia, measured range, azimuth, elevation,
Doppler, radar cross section, target type and target identity
properties.
The conceptualisation of a world of objects with properties
is a fundamental characteristic of every object assessment,
and its development can be tracked historically. From about
600 BC, the ancient Greeks had conceived of a world of
objects (things) manipulated by the actions of gods. With the
advent of the Presocratics, the assortment of gods gradually
gave way to an understanding based in nature, but the newer
outlooks remained rooted in a world of objects. Around 350
BC, Aristotle [11] refined the view by proposing that objects
were composed of both form (properties) and matter, id est,
objects were formed matter. Matter is the "stuff" of which a
thing is composed, the characteristic that makes a ship this
ship, rather than that ship. Form is that which determines
what a thing is, the characteristic that makes a ship, a ship.
Objects therefore had properties or forms associated with
them, and through the persistence of certain “essential”
forms, an object could remain the same object while some of
its non-essential forms changed.
Modern object assessments are based upon this Aristotelian
conceptualisation. The track data structures within figure 1
are indicative. Each individual data structure element
represents an instance of an object’s matter in the world. The
field names within those data structures represent the forms
or properties of interest, while the numerical values assigned
to those fields are measures of those properties for that
object instance. A new (update) instance of that object
(track) is identified as an instance of that same object if its
measured properties conform to the essential properties
identified for that type of object.
3.2 Situations for Situation Assessments
Aristotle’s conception of a world of objects endured
throughout medieval times, receiving heavy-handed
endorsement from the Church during the scholastic period.
Indeed, it was only little more than two centuries ago that
challenges first began to surface. Olson [12] documents the
transition. The impetus for change was the emergence of
relations as a conception over and above properties, and it
arose because of limitations in the Aristotelian outlook.
Relations facilitate greater expressibility, provide a basis for
knowledge representation, and appear to have a
neurophysiological basis. Lambert [13] briefly explores each
of these issues and notes that the Aristotelian limitations
equally apply to object fusion. The emergence of the idea of
a relation culminated with Ludwig Wittgenstein, who first
explicitly proposed a world of facts as the fundamental
substrate, where facts are subsequently understood as the
application of relations to objects. In his cryptic,
unapologetic style, Wittgenstein launched his 1922
publication of Tractatus Logico-Philosophicus [14] with the
words
1. The world is all that is the case.
1.1 The world is the totality of facts, not of things.
Wittgenstein supplanted a view that had persisted for over
2000 years. This fundamental shift in human
conceptualisation underpins the difference between “level 1”
and “level 2” fusion.
Object assessments assess a world of objects. Situation
assessments assess a world of facts – almost! When
engaging the world, we rarely attend to individual facts in
isolation. Typically we form mental snapshot pictures of the
world over some limited time frame and region, and in
assessing this picture we are naturally inclined to represent it
as a collection of facts. I term these collections of facts,
events. We are also inclined to associate collections of these
events when comprehending the world. These collections of
events I term scenarios. The term situation is applied to
mean an event or a scenario. Barwise and Perry [15]
proposed that a similar notion of situation was the
fundamental building block of our assessment of the world.
They stated,
Reality consists of situations - individuals having
properties and standing in relations at various spatiotemporal locations.
Situations are essentially collections of related spatiotemporal facts, where facts consist of relations between
objects and are expressed symbolically through sentences.
This is a step up from Wittgenstein’s world of facts. Here
the world is a world of situations, and assessing the world
involves individuating situations. Situation assessment
involves assessing situations, not facts or objects per se.
4 An Example Situation Assessment
4.1 A Simple Situation Assessment
Under Endsley’s conception, situation awareness is a state of
knowledge about aspects of interest in the world. Situation
assessments, I contend in section 2, refer to the
comprehension component of that knowledge, while
machine situation assessments are sentences formed through
an automated process and are used to express beliefs about
the world. A good situation assessment captures what a
human might believe of a situation.
assessments are generated from the interaction
environmental, definitional and domain beliefs.
of
To illustrate a situation assessment product, the presentation
accompanying this paper includes a digitised video segment
from the movie “The Hunt for Red October”. The segment
can be conceptualised through a sequence of seven events:
• aircraft detects submarine;
• submarine detects aircraft;
• aircraft drops torpedo;
• submarine deploys countermeasures;
• submarine countermeasures fail;
• torpedo approaches submarine;
• submarine turns.
Figure 3 presents a single image for each of these events.
The sequence of seven phrases describing the seven events is
itself a situation assessment of the video sequence, though
one expressed at a very coarse level of abstraction.
4.2 A More Detailed Situation Assessment
1. aircraft detects submarine
3. aircraft drops torpedo
5. sub countermeasures fail
2. submarine detects aircraft
4. sub deploys countermeas.
6. torpedo approaches sub
7. submarine turns
Figure 3 : Images from “The Hunt for Red October”
Three kinds of belief are necessary for (machine) situation
fusion. Environmental beliefs arise from direct observation
of the world at a given time, such as my belief that there is
text on this page. Definitional beliefs express the meaning of
terms, such as the belief that time is ordered. Domain beliefs
express how we presume the world to be independently of a
direct observation, such as my belief that an active torpedo
striking a submarine will cause an explosion. Situation
A more detailed situation assessment requires us to say
something further about the content of each of these events.
This involves having a richer ontology, being a conceptual
framework for assessing the world, together with a means of
specifying it [16]. To illustrate, an extension of the theory of
processes developed by Lambert [17] is used. On this view,
reality is understood as a monistic metaphysics of processes.
Ω denotes reality – the universe process. Fragments of
reality are typically composed of other fragments. x being a
fragment of y is represented by x ≤ y. The axioms for ≤
identify the ontological structure of processes as a Boolean
algebra <Ω; +, •, −, ⊥, Ω>. Ω represents everything; ⊥
represents nothing; + is a join operator; • is a meet operator;
and − is a complement operator. These operators allow us to
describe parts of reality in terms of composite parts.
Time is ontologically embedded within the process Boolean
algebra by introducing a time function that defines time(x)
as the maximal process that coexists with x. Temporal
processes are then the fixed points of the time function.
Ontologically, this makes temporal processes a subalgebra of
the Boolean algebra of processes. The diachronic dimension
to time is handled through the introduction of an unbounded,
discrete, linear temporal ordering relation ∠. start and
finish functions can then be defined for each process and
they in turn can be used to define a duration function.
Periods are defined as the fixed points of the duration
function. All but the last of the Hayes and Allen [18]
temporal axioms are derivable as theorems, where ; is the
meet operator identifying temporal contiguity between two
periods.
<Ω; +, •, −, ⊥, Ω> is a Boolean algebra
∀x ∀y (x + y ≡ y + x) & ∀x ∀y (x • y ≡ y • x).
∀x ∀y (((x • y) + y) ≡ y) & ∀x ∀y (((x + y) • y) ≡ y).
∀x ∀y ∀z (((x + y) + z) ≡ (x + (y + z))) &
∀x ∀y ∀z (((x • y) • z) ≡ (x • (y • z))).
∀x ∀y (((x • −x) + y) ≡ y) & ∀x ∀y (((x + −x) • y) ≡ y).
<{x | temp(x)}; +, −> is a subalgebra
temp(x) iff time(x) ≡ x.
∀x ∀y ((temp(x) & temp(y)) ⇒ temp(x + y)).
∀x (temp(x) ⇒ temp(−x)).
∀x ∀y ((temp(x) & temp(y)) ⇒ temp(x • y)).
∀x ∀y ((temp(x) & temp(y)) ⇒ temp(x - y)).
Hayes Allen (1987) axioms as theorems
period(x) iff duration(x) ≡ x.
x ; y iff period(x) & period(y) &
∀u (temporal(u) ⇒ (finish(x) ∠ u ⇔ start(y) ∠ u)).
Substitutional instances for p, q, r, t and s are periods :
∀p ∀q ∀r ∀s ((p ; q & p ; s & r ; q) ⇒ r ; s).
∀p ∀q ∀r ∀s ((p ; q & r ; s)
⇒ (p ; s ∨ ∃t (p ; t & t ; s) ∨ ∃t (r ; t & t ; q))).
∀p ∃q ∃r (q ; p & p ; r).
∀p ∀ q ∀r ∀s ((p ; q & q ; s & p ; r & r ; s) ⇒ q ≡ r).
∀p ∀q (p ; q ⇒
∃r ∃s (r ; p & q ; s & r ; (p + q) & (p + q) ; s)).
Closure Axioms: (Adapted from Kuratowski)
⊥ ≡ ⊥.
∀x (x ≤ x).
∀x (x ≡ x).
∀x ∀y (x + y ≡ x + y).
attached(x, y) iff x ≤ y.
<{x | enclos(x)}; +, •> is a sublattice
enclos(x) iff x ≡ x.
enclos(⊥).
enclos(Ω).
∀x ∀y ((enclos(x) & enclos(y)) ⇒ enclos(x + y)).
∀x ∀y ((enclos(x) & enclos(y)) ⇒ enclos(x • y)).
Figure 4 : Outline of a theory of processes
Space can be conceptualised as Euclidean space through the
imposition of Euclidean axioms for points, lines and planes
[19]. As the Boolean algebra of processes is an atomic
Boolean algebra in [17], points and atoms coincide to
identify lines and planes as processes. A weaker conception
of spatial attachment can also be applied. An item x placed
in a box y is attached to box y without being a fragment of it.
By introducing a closure operator _ and Kuratowski’s
topological axioms, we can express the spatial attachment of
x to y by x ≤ y. Enclosures can then be identified as fixed
points of the closure operator _, and form a sublattice of the
process algebra. Collectively, all of the spatio-temporal
process axioms alluded to define some of the definitional
beliefs required, and thereby give meaning to the assertions
made about the events of figure 3.
aircraft detects submarine
{propeller_plane(a•t), label(a•t,”BEAR FOXTROT #692”),
soviet(a•t),
b•t≤a•t,
sonar_operator(b•t),
c•t≤a•t,
believes(b•t12,detect(c•t11,d•t11)),
sonar_equipment(c•t),
says(b•t13,“Captain …”), t1≤t, t11≤t1, t12≤t1, t13≤t1, t11∠t12,
t12∠t13}.
submarine detects aircraft
{submarine(d•t), label(d•t,”RED OCTOBER”), ocean(o•t),
∀u(u∈d•t⇒∃v∃w(v∈∂o•r•t&uw<+v)),
air(r•t),
∀u(u∈a•t⇒∃v∃w(v∈∂o•r•t&uw<-v)), soviet(d•t), e•t≤d•t,
f•t≤d•t,
sonar_operator(e•t),
sonar_equipment(f•t),
g•t21≡a•t21,
believes(e•t22,detect(f•t21,g•t21)),
says(e•t23,“Captain, sonar, we’ve just been overflown by a
low altitude multi-engine turbo prop.”), h•t≤d•t,
believes(h•t24,detect(f•t21,i•t21)),
captain(h•t),
sonar_buoys(i•t),
believes(h•t25,threaten(g•tx,d•tx)),
says(h•t26,“Battlestations.”), t2≤t, t21≤t2, t22≤t2, t23≤t2, t24≤t2,
t25≤t2, t26≤t2, t1∠t2, t21∠t22, t22∠t23, t23∠t24, t24∠t25, t25∠t26, t2∠tx}.
Figure 5 : Formal events for events 1 and 2 in Figure 3
Using this framework, figure 5 formally conceptualises the
first two events of figure 3. Both sets of formal sentences in
figure 5 define a formal event. In the ATTITUDE multi-agent
reasoning system, formal events are supported as a primitive
expression type and are used to reference partitions of the
knowledge base. Boolean algebra combinations of (formal)
events are created as (formal) scenarios, which serve as
another primitive expression type. (Formal) events and
scenarios can be dynamically altered within ATTITUDE.
Again, the term situation is applied collectively to events and
scenarios. ATTITUDE’s (Bayesian) Horn clause inference
engine supports contextual reasoning over the knowledge
base relative to situations.
To illustrate, suppose formal events describing the events of
figure 3 are referenced in ATTITUDE through the variables:
• ?submarine_detection;
• ?aircraft_detection;
• ?torpedo_dropped;
• ?countermeasures_deployed;
• ?countermeasures_fail;
• ?torpedo_approaches;
• ?submarine_turns.
Each references a partition of the knowledge base. A
scenario ?submarine_attack might then be formed by
matching variable ?submarine_attack to
(+ ?submarine_detection ?aircraft_detection
?torpedo_dropped ?countermeasures_deployed
?countermeasures_fail ?torpedo_approaches
?submarine_turns).
awareness. Situation and impact assessments therefore
emerge as necessary elements, by respectively providing the
technological aspects of the comprehension and projection
elements of situation awareness.
The events of figure 3 can then be reasoned about by
querying relative to the scenario ?submarine_attack.
Machine comprehension of these events may lead the
ATTITUDE system to project two possible outcome scenarios.
The first sees the torpedo strike the submarine, resulting in
the submarine exploding. The second sees the torpedo miss
the submarine, resulting in the adjacent sea canyon
exploding. In the presentation, video footage is shown for
both outcome scenarios and images for each of the four
events mentioned appears in figure 6. By forming
?submarine_destroyed =
(+ ?torpedo_strike ?submarine_explodes) and
?submarine_survives =
(+ ?torpedo_misses ?canyon_explodes)
as formal situations describing the two projected scenario
outcomes, the ATTITUDE system is able to formally describe
and reason about the two projected situations
?Red_October_survives_attack =
(+ ?submarine_attack ?submarine_destroyed) and
?Red_October_destroyed_by_attack =
(+ ?submarine_attack ?submarine_destroyed).
With this understanding, the paper then considers the nature
of situation assessments. It recognises that the numericalsymbolic demarcation typically evident between object and
situation assessments, is in fact reflective of a broader
historical paradigm shift in human conceptualisation.
Situation assessments emerge as stories about the world,
couched in formal languages capable of expressing
relationships between objects. A concept of situation is
proposed as the basis by which we organise these stories. A
theoretical process ontology is discussed for defining the
language of situations and an existing computational
implementation of situations is illustrated. Situations emerge
as a conceptual foundation for situation assessments, that in
turn deliver a technological element of situation awareness.
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11. sea canyon explodes.
Figure 6 : Images of possible events from torpedo attack
4 Conclusion
The currently dominant conceptualisation of common
operating picture falls well short of our situation awareness
requirements, in part because it fails to address the
comprehension and projection aspects of situation
awareness. By reconciling situation awareness and data
fusion, the paper contends that situation awareness is a
product of mental data fusion, and that machine data fusion
provides a sufficient technological basis for situation
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Edited L. Reznik and V. Kreinovich. Studies in Fuzziness
and Soft Computing, Physica Verlag.
(Thanks to Paul Taplin for undertaking the video editing.)