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Evaluating the accomplishments of the cooperative threat reduction program.Grams, Stacy A. January 2000 (has links) (PDF)
Thesis (M.A. in National Security Affairs) Naval Postgraduate School, Dec. 2000. / Thesis advisor, David S. Yost. Includes bibliographical references. Also available online.
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Evaluation and Implementation of Traceable Uncertainty for Threat EvaluationHaglind, Carl January 2014 (has links)
Threat evaluation is used in various applications to find threatening objects or situations and neutralize them before they cause any damage. To make the threat evaluation as user-friendly as possible, it is important to know where the uncertainties are. The method Traceable Uncertainty can make the threat evaluation process more transparent and hopefully easier to rely on. Traceable Uncertainty is used when different sources of information are combined to find support for the decision making process. The uncertainty of the current information is measured before and after the combination. If the magnitude of uncertainty has changed more than a threshold, a new branch will be created which excludes the new information from the combination of evidence. Traceable Uncertainty has never been tested on any realistic scenario to investigate whether it is possible to implement the method on a large scale system. The hypothesis of this thesis is that Traceable Uncertainty can be used on large scale systems if its threshold parameter is tuned in the right way. Different threshold values were tested when recorded radar data were analyzed for threatening targets. Experiments combining random generated evidence were also analyzed for different threshold values. The results showed that a threshold value in the range [0.15, 0.25] generated a satisfying amount of interpretations that were not too similar to eachother. The results could also be filtered to take away unnecessary interpretations. This shows that in this aspect and for this data set, Traceable Uncertainty can be used on large scale systems.
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Evaluating the performance of TEWA systemsJohansson, Fredrik January 2010 (has links)
It is in military engagements the task of the air defense to protect valuable assets such as air bases from being destroyed by hostile aircrafts and missiles. In order to fulfill this mission, the defenders are equipped with sensors and firing units. To infer whether a target is hostile and threatening or not is far from a trivial task. This is dealt with in a threat evaluation process, in which the targets are ranked based upon their estimated level of threat posed to the defended assets. Once the degree of threat has been estimated, the problem of weapon allocation comes into the picture. Given that a number of threatening targets have been identified, the defenders need to decide on whether any firing units shall be allocated to the targets, and if so, which firing unit to engage which target. To complicate matters, the outcomes of such engagements are usually stochastic. Moreover, there are often tight time constraints on how fast the threat evaluation and weapon allocation processes need to be executed. There are already today a large number of threat evaluation and weapon allocation (TEWA) systems in use, i.e. decision support systems aiding military decision makers with the threat evaluation and weapon allocation processes. However, despite the critical role of such systems, it is not clear how to evaluate the performance of the systems and their algorithms. Hence, the work in thesis is focused on the development and evaluation of TEWA systems, and the algorithms for threat evaluation and weapon allocation being part of such systems. A number of algorithms for threat evaluation and static weapon allocation are suggested and implemented, and testbeds for facilitating the evaluation of these are developed. Experimental results show that the use of particle swarm optimization is suitable for real-time target-based weapon allocation in situations involving up to approximately ten targets and ten firing units, while it for larger problem sizes gives better results to make use of an enhanced greedy maximum marginal return algorithm, or a genetic algorithm seeded with the solution returned by the greedy algorithm. / Fredrik Johansson forskar också vid Skövde Högskola, Informatics Research Centre / Fredrik Johansson also does research at the University of Skövde, Informatics Research Centre
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The Chavez corollary the new hegemony on the block /McLaughlin, James A. January 2008 (has links)
Thesis (Master of Military Studies)-Marine Corps Command and Staff College, 2008. / Title from title page of PDF document (viewed on: Dec 29, 2009). Includes bibliographical references.
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