Estimating the relative value of alternative tactics, techniques and procedures (TTP) and information systems requires
measures of the costs and benefits of each, and methods for combining and comparing those measures. The NATO Code
of Best Practice for Command and Control Assessment explains that decision making quality would ideally be best
assessed on outcomes. Lessons learned in practice can be assessed statistically to support this, but experimentation with
alternate measures in live conflict is undesirable. To this end, the development of practical experimentation to
parameterize effective constructive simulation and analytic modelling for system utility prediction is desirable. The Land
Battlespace Systems Department of Dstl has modeled human development of situational awareness to support
constructive simulation by empirically discovering how evidence is weighed according to circumstance, personality,
training and briefing. The human decision maker (DM) provides the backbone of the information processing activity
associated with military engagements because of inherent uncertainty associated with combat operations. To develop
methods for representing the process in order to assess equipment and non-technological interventions such as training
and TTPs we are developing componentized or modularized timed analytic stochastic model components and
instruments as part of a framework to support quantitative assessment of intelligence production and consumption
methods in a human decision maker-centric mission space. In this paper, we formulate an abstraction of the human
intelligence fusion process from the Defence Science and Technology Laboratory's (Dstl's) INCIDER model to include
in our framework, and synthesize relevant cost and benefit characteristics.
A group of acoustic arrays that provide direction of approach estimates also support classification of vehicles using
the beams formed during that estimation. Successful simultaneous tracking and classification has demonstrated
the value of such a sensing resource as a UGS installation. We now consider potential attacks on the integrity of
such an installation, describing the effect of compromised acoustic arrays in the data analysis and tracking and
classification results. We indicate how these can be automatically recognized, and note that calibration methods
intended for deployment time can be used for recovery during operation, which opens the door to methods for
recovery from the compromise without re-configuring the equipment, using abductive reasoning to discover the
necessary re-processing structure.
By rotating an acoustic array, the tracking stability and implied path of a tracked entity can be distorted
while leaving the data and analysis from individual arrays self-consistent. Less structured modifications, such as
unstructured re-ordering of microphone connections, impact the basic data analysis. We examine the effect of
these classes of attack on the integrity of a set of unattended acoustic arrays, and consider the steps necessary
for detection, diagnosis, and recovering an effective sensing system. Understaning these steps plays an important
part in reasoning in support of balance of investment, planning, operation and post-hoc analysis.
Planning a mission to monitor, control or prevent activity requires postulation of subject behaviours, specification of
goals, and the identification of suitable effects, candidate methods, information requirements, and effective
infrastructure. In an operation that comprises many missions, it is desirable to base decisions to assign assets and
computation time or communications bandwidth on the value of the result of doing so in a particular mission to the
operation. We describe initial investigations of a holistic approach for judging the value of candidate sensing service
designs by stochastic modeling of information delivery, knowledge building, synthesis of situational awareness, and the
selection of actions and achievement of goals. Abstraction of physical and information transformations to interdependent
stochastic state transition models enables calculation of probability distributions over uncertain futures using wellcharacterized
approximations. This complements traditional Monte Carlo war gaming in which example futures are
explored individually, by capturing probability distributions over loci of behaviours that show the importance and value
of mission component designs. The overall model is driven by sensing processes that are constructed by abstracting from
the physics of sensing to a stochastic model of the system's trajectories through sensing modes. This is formulated by
analysing probabilistic projections of subject behaviours against functions which describe the quality of information
delivered by the sensing service. This enables energy consumption predictions, and when composed into a mission
model, supports calculation of situational awareness formulation and command satisfaction timing probabilities. These
outcome probabilities then support calculation of relative utility and value.
The output of a sensor network intended to detect events or objects generally comprises evidentiary reports of features in
the environment that may correspond to those phenomena. Signals from multiple sensors are commonly fused to
maximize fidelity of detection through for example synergy between different modes of detection, or simple
confirmation. We have previously demonstrated the ability to calculate the meaning of a location report as a probability
distribution over potential ground truths by using a stochastic process algebraic model compiled to a discrete-state,
continuous-time Markov chain, and performing a transient analysis which resembles the process of parameterizing a
Bayesian network. We introduce an approach to representing temporal fusion of multiple heterogeneous sensor
detections with different modalities and timing characteristics using a stochastic process algebra. This facilitates analysis
of probabilistic properties of the system, and inclusion of those properties into larger models. The formal models are
translated into continuous time Markov chains, which provide an important trade-off between the approximation of
timing information against complexity of analysis. This is vital to the investigation of analytic computation in real world
problems. We illustrate this with an example detection-oriented sensing service model emphasizing the impact of timing.
Detection probability and confidence is an essential aspect of the quality of information delivered by a sensing service.
The present work is part of an effort to develop a formal event detection calculus that captures the essence of sensor
information relating to events, such that features and dependencies can be exploited in re-usable, extendible
compositional models.
In a typical military application, a wireless sensor network will operate in diffcult and dynamic conditions.
Communication will be affected by local conditions, platform characteristics and power consumption constraints,
and sensors may be lost during an engagement. It is clearly of great importance to decision makers to know what
quality of information they can expect from a network in battlefield situations. We propose the development
of a supporting technology founded in formal modeling, using stochastic process algebras for the development
of quality of information measures. A simple example illustrates the central themes of outcome probability
distribution prediction, and time-dependency analysis.
Conference Committee Involvement (3)
Modeling and Simulation for Defense Systems and Applications VII
24 April 2012 | Baltimore, Maryland, United States
Modeling and Simulation for Defense Systems and Applications VI
26 April 2011 | Orlando, Florida, United States
Modeling and Simulation for Defense Systems and Applications V
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