Analysts often use inter-unit demand for communication services as the basis for assessing network performance
and the impact on mission effectiveness. Traditional methods base inter-unit demand on Information Exchange
Requirements (IER's) most often derived from a variety of disparate sources that can result insignificant limitations. This paper describes models and algorithms that enable automated support for the challenging steps of tailoring the data from an established unit demand database in order to derive the inter-unit demand for specific scenarios. Such a capability is referred to as "demand parsing." The necessary operational constraints are
modeled by applying an organizational distance metric, using weights associated with a small set of functionally
driven usage patterns, to a node-link structure established at a level of resolution appropriate for the analytical
context. An innovative agent based algorithm is applied to address the resulting multi-objective optimization problem by calculating solutions that satisfy both the operational constraints and those imposed by the unit demand. Using an agent based paradigm, the operational model and the algorithm were combined into a prototype tool that was applied within the parsing process to estimate the inter-unit demand for communications supporting units in a realistic air operation. The peak errors in meeting both types of constraints were found to be less than 20%. These levels are consistent with the errors in the unit database intended for first order assessments.
This is another in a sequence of papers reporting on the development of innovative methods and tools for estimating demand requirements for network supply capabilities. An extension of the demand estimation methodology,
this paper focuses on steps required to assess the adequacy of performance of candidate networks by means of an
integrated tool. The steps include mapping units in a scenario to units in the associated database to determine
their aggregate demand, developing an appropriate logical network with computational constraints dictated by
the scenario, and calculating inter-unit demand of the units in the logical network. Because of the complexity of
the end-to-end process, assuring repeatability while facilitating rapid exploration of issues is a challenge. Earlier
tools implementing this process were fragmented and prone to error, requiring significant analyst effort to accomplish even the smallest changes. To address these limitations, the process has been implemented in an easy to use, integrated tool. This allows complete
exibility in manipulating data and promotes rapid, but repeatable
analyses of tailored scenarios.
Finding certain associated signals in the modern electromagnetic environment can prove a difficult task due to signal
characteristics and associated platform tactics as well as the systems used to find these signals. One approach to finding
such signal sets is to employ multiple small unmanned aerial systems (UASs) equipped with RF sensors in a team to
search an area. The search environment may be partially known, but with a significant level of uncertainty as to the
locations and emissions behavior of the individual signals and their associated platforms. The team is likely to benefit
from a combination of using uncertain a priori information for planning and online search algorithms for dynamic
tasking of the team. Two search algorithms are examined for effectiveness: Archimedean spirals, in which the UASs
comprising the team do not respond to the environment, and artificial potential fields, in which they use environmental
perception and interactions to dynamically guide the search. A multi-objective genetic algorithm (MOGA) is used to
explore the desirable characteristics of search algorithms for this problem using two performance objectives. The results
indicate that the MOGA can successfully use uncertain a priori information to set the parameters of the search
algorithms. Also, we find that artificial potential fields may result in good performance, but that each of the fields has a
different contribution that may be appropriate only in certain states.
This work investigates the efforts behind defining a classification system for multi-agent search and tracking problems,
specifically those based on relatively small numbers of agents. The pack behavior search and tracking classification
(PBSTC) we define as mappings to animal pack behaviors that regularly perform activities similar to search and
tracking problems, categorizing small multi-agent problems based on these activities. From this, we use evolutionary
computation to evolve goal priorities for a team of cooperating agents. Our goal priorities are trained to generate
candidate parameter solutions for a search and tracking problem in an emitter/sensor scenario. We identify and isolate
several classifiers from the evolved solutions and how they reflect on the agent control systems's ability in the
simulation to solve a task subset of the search and tracking problem. We also isolate the types of goal vector parameters
that contribute to these classified behaviors, and categorize the limitations from those parameters in these scenarios.
The role of an airborne electronic attack (AEA) system-of-systems (SoS) is to increase survivability of friendly aircraft
by jamming hostile air defense radars. AEA systems are scarce,
high-demand assets and have limited resources with
which to engage a large number of radars. Given the limited resources, it is a significant challenge to plan their
employment to achieve the desired results. Plans require specifying locations of jammers, as well as the mix of
wide-
and narrow-band jamming assignments delivered against particular radars. Further, the environment is uncertain as to
the locations and emissions behaviors of radars. Therefore, we require plans that are not only capable, but also robust to
the variability of the environment. In this paper, we use a
multi-objective genetic algorithm to develop capable and
robust AEA SoS mission plans. The algorithm seeks to determine the Pareto-front of three objectives - maximize the
operational objectives achieved by friendly aircraft, minimize the threat to friendly aircraft, and minimize the
expenditure of AEA assets. The results show that this algorithm is able to provide planners with the quantitative
information necessary to intelligently construct capable and robust mission plans for an AEA SoS.
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