A key to mastering asymmetric warfare is the acquisition of accurate intelligence on adversaries and their assets in urban
and open battlefields. To achieve this, one needs adequate numbers of tactical sensors placed in locations to optimize
coverage, where optimality is realized by covering a given area of interest with the least number of sensors, or covering
the largest possible subsection of an area of interest with a fixed set of sensors. Unfortunately, neither problem admits a
polynomial time algorithm as a solution, and therefore, the placement of such sensors must utilize intelligent heuristics
instead. In this paper, we present a scheme implemented on parallel SIMD processing architectures to yield significantly
faster results, and that is highly scalable with respect to dynamic changes in the area of interest. Furthermore, the
solution to the first problem immediately translates to serve as a solution to the latter if and when any sensors are
rendered inoperable.
Appreciating terrain is a key to success in both symmetric and asymmetric forms of warfare. Training to enable Soldiers
to master this vital skill has traditionally required their translocation to a selected number of areas, each affording a
desired set of topographical features, albeit with limited breadth of variety. As a result, the use of such methods has
proved to be costly and time consuming. To counter this, new computer-aided training applications permit users to
rapidly generate and complete training exercises in geo-specific open and urban environments rendered by high-fidelity
image generation engines. The latter method is not only cost-efficient, but allows any given exercise and its conditions to
be duplicated or systematically varied over time. However, even such computer-aided applications have shortcomings.
One of the principal ones is that they usually require all training exercises to be painstakingly constructed by a subject
matter expert. Furthermore, exercise difficulty is usually subjectively assessed and frequently ignored thereafter. As a
result, such applications lack the ability to grow and adapt to the skill level and learning curve of each trainee. In this
paper, we present a heuristic that automatically constructs exercises for identifying key terrain. Each exercise is created
and administered in a unique iteration, with its level of difficulty tailored to the trainee's ability based on the correctness
of that trainee's responses in prior iterations.
As militaries across the world continue to evolve, the roles of humans in various theatres of operation are being
increasingly targeted by military planners for substitution with automation. Forward observation and direction of
supporting arms to neutralize threats from dynamic adversaries is one such example. However, contemporary tracking
and targeting systems are incapable of serving autonomously for they do not embody the sophisticated algorithms
necessary to predict the future positions of adversaries with the accuracy offered by the cognitive and analytical abilities
of human operators. The need for these systems to incorporate methods characterizing such intelligence is therefore
compelling. In this paper, we present a novel technique to achieve this goal by modeling the path of an entity as a
continuous polynomial function of multiple variables expressed as a Taylor series with a finite number of terms. We
demonstrate the method for evaluating the coefficient of each term to define this function unambiguously for any given
entity, and illustrate its use to determine the entity's position at any point in time in the future.
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