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1、畢業(yè)設計(論文)英文資料翻譯外文資料翻譯 外文資料翻譯系 別:專 業(yè):班 級:姓 名:學 號:指導老師:2014 年 4 月西北工業(yè)大學明德學院本科畢業(yè)設計論文 西北工業(yè)大學明德學院本科畢業(yè)設計論文 2problems in combat decision making. Therefore it is no surprise that it was called upon to solve
2、 the problem of planning and optimizing the use of soft-kill in modern combat systems. Decision algorithms for planning soft-kill deployment are based on detailed modeling and simulation of the combat scenario that take
3、into account the information about the threat, ship, soft-kill component and environment. An operational system based on these principles is the SKWS from Terma advertised in. In order to determine the best soft-kill dep
4、loyment solution, these systems use many data sources such as threat databases, data about the signatures of the ship, environment data and others. The details of how these systems use the available data to arrive at the
5、 recommended deployment solution may be quite different from a system to the other. What each of them have in common is that they are based on predicting the effect of the planned measure on the attacking missile. Clearl
6、y without a way to predict what will happen if a certain soft-kill measure is taken, it is impossible to choose the best solution. Obviously, the quality of the prediction will have an immediate effect on the performance
7、 of the system. Computational time constraints will necessarily limit the complexity of the performance estimation algorithm, so many factors that may influence the effect of the soft-kill measure will need to be neglect
8、ed or approximated. Perhaps a more important aspect that is characteristic for the conditions in which these systems need to work is the inherent presence of information uncertainty. None of the systems available at this
9、 moment allows one to take this uncertainty into account while predicting the effect of the deployment. However, correct decision making is hardly possible without quantifying the uncertainty.The present work is based on
10、 the idea of using adjoint simulation techniques to address both challenges: to reduce dramatically the computational effort and to enable taking into account the information uncertainty in the prediction of the effect o
11、f the soft-kill system. Two possible applications are envisaged for using the proposed prediction method. Firstly, before launch, the best launch parameters can be chosen based on predicting the effect of the deployment.
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