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The purpose of this research is to investigate the effectiveness of wide-area search munitions in various scenarios using different cooperative behavior algorithms. The general scenario involves multiple autonomous munitions searching for an unknown number of targets of different priority in unknown locations. Three cooperative behavior algorithms are used in each scenario: no cooperation, cooperative attack only, and cooperative classification and attack. In the cooperative cases, the munitions allocate tasks on-line as a group, using linear programming techniques to determine the optimum allocation. Each munition provides inputs to the task allocation routine in the form of probabilities of successfully being able to complete the various tasks. These probabilities of success are based on statistical Poisson field theory. Weighting parameters are applied to the probabilities of success so that optimum settings can be determined via Response Surface Methodology. Results are compared within and across the various scenarios. Initial results did not reflect expected behavior (due to poor choice of responses to optimize). Experiments were modified and more desirable results obtained. In general, cooperative engagement alone attacks and kills fewer targets than no cooperation. Cooperative classification however, kills fewer targets at low false target attack rates (less than 0.005/km2), but outperforms the other algorithms as the false target attack rate increases. This is due primarily to the fact that cooperative classification significantly reduces and stabilizes the effective false target attack rate.
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Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.