IEEE Systems, Man, and Cybernetics Magazine - January 2018 - 34

into computer-executable code that then generates the
measures of effectiveness and measures of performance)
required sample-path data, outlining procedures for conthat support course-of-action decisions.
verting these data into estimates of system performance,
Related approaches have been applied by Jousselme to
and then illustrating methods for assessing how well these
address the challenge of sensor fusion for object identificaestimates approximate true but unknown system behavtion under uncertainty [7]-[9]. For this analysis, fusion sceiors. This concept of modeling complex systems provides a
narios are defined and data from diverse sources are fused
relatively low-cost way of gathering information for deciin an attempt to improve situational understanding and
sion making [1], [2].
better support decision making. Various uncertainty
There are a number of DES tools available that enable
approaches are applied, including epistemic (to address
the generation of a system model and the execution of
the imperfection of sources), aleatory (to address the variexperiments using that model as the system progresses
ability of event occurrences), and classic Bayesian probathrough time. Models enable a demonstration of how a
bility and Dempster reasoning theories. However, none of
real-world activity will perform under different conditions
these provides an objective quantification result from
and facilitate the testing of various hypotheses. A princiwhich the quality of the data fusion product can be
pal benefit of a DES model is that it provides a simple
derived, i.e., a high confidence level that the fused data are
approximation of a process and enables a gradual refineaccurate with respect to achieving the mission data-qualiment of the model as understanding of the process
ty objectives required to compute Ps .
improves. This stepwise refinement achieves adequate
Other approaches for object recognition and location in
approximations of complex problems. An example of a
diverse geographic regions use advanced satellite, radar,
DES tool is ExtendSim [3], [4], with
and ISR capabilities to collect and
which one can develop dynamic
then fuse mission data  [10]. The
models of real-life processes in a
limitations of this approach in A DES model
wide variety of fields. ExtendSim
clude the fact that the correlation
may be used to create models from
of the collected data is weak, the
provides a simple
building blocks, explore the proresults are not statistically comapproximation of a
cesses involved, and see how they
puted, and (as additional ISR
process and enables
relate. If the initial assumptions
assets and staff are applied to idenchange, then the inputs to Extendtify objects) redundant tasking and
a gradual refinement
Sim can be f lexibly altered to
inefficient mission planning are
of the model as
derive a refined solution.
likely to occur  [11]. Big-data comDES alone, however, may not
panies, such as IBM and Google,
understanding of the
be powerful enough to provide a
are also working on the problem
process improves.
comprehensive analytic answer to
but have not achieved a comprethe problem. For example, Ps in a
hensive solution for finding an
traditional missile-defense scearbitrary object in an unspecified
nario is based on the probability of a single-shot engagegeographical area [12], [13]. Crowdsourcing via the Interment success (Pssek), which expresses the reliability of
net is a growing trend. DigitalGlobe, for example, prothe combat system operating correctly and the probabilivides global imagery coverage on a historical basis but
ty of the interceptor (i.e., the technique) neutralizing the
does not offer every possible image for everywhere in the
threat target (i.e., the object of interest). Pssek is derived
world on every day [14], [15]. Problems with crowdsourcby considering kinetic techniques (e.g., the Standard Mising include accessibility, false reports, denial of service,
sile 6 interceptor) to neutralize the ballistic missile threat
and the fact that the majority of users are not trained and
target [5]. However, current methods for calculating Pssek
that adversaries could sabotage results. The AMADS proare notional at best and inaccurate. They promote comvides comprehensive solutions to a variety of complex
plex solutions that result in low system reliability and a
data collection and data fusion scenarios and supplies
correspondingly low Pssek . Scoring systems exist to
end users with statistically computed results that are
assess the vulnerabilities of kinetic weapon targets (e.g.,
accessible, secure, and understandable.
missiles) and the corresponding success of various
effects (i.e., techniques) to defeat these targets. One such
Approach
scoring system is the Common Vulnerability Scoring SysThe new AMADS approach is based on the architectural
tem (CVSS), which provides an open framework within
diagram in Figure 1, which shows the activity flow for
which to score and match vulnerabilities and effects [6].
the  adaptive components for the missile-defense and
However, CVSS does not provide a method by which to
remote-object-location mission scenarios. The f low
convert these scores into probability distributions. There
begins at the top left of the figure with an initial activity,
is a critical need for new and more comprehensive analytsuch as the application of a technique to defeat a threat
ic approaches that translate scores into metrics (i.e.,
target or an object algorithm to detect and locate an
34

IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE Janu ar y 20 18



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