传感器优先级策略的多维奖励量

M. Rahmes, Rick Pemble, G. Lemieux, K. Fox
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引用次数: 3

摘要

我们描述了一个模型,用于确定传感器优先级策略的决策策略。我们将运筹学方法和遥感相结合,用于决策制定几种可能的行动,世界状态和混合概率度量。我们执行数据挖掘和发现模式,从多个传感器输入自动增强态势感知。我们的解决方案是为确定可操作信息的方法而开发的,以有效地管理遥感资产和使用开源信息。我们的工具允许计算最优策略,提供关于世界状态的更多知识,并增加决策者做出最佳决策的可能性。本文用线性规划方法讨论了多维博弈理论中多种可能策略的求解问题。我们讨论了奖励量的新概念。决策者的成功取决于他正确和自动判断多种因素的能力。我们的系统名为GlobalSite,其目标是提供可信赖的威胁分析系统和服务,以了解情况,同时成为持续任务操作的重要工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-dimensional reward volumes for sensor priority strategies
We describe a model for determining strategies for making decisions for sensor prioritization strategies. We combine operations research methods and remote sensing for decision making with several possible actions, state of the world, and a mixed probability metric. We perform data mining and discover patterns to automatically enhance situational awareness from multiple sensor inputs. Our solution has been developed for a method for determining actionable information to efficiently manage remote sensing assets and use open source information. Our tool allows for calculating optimal strategies, provides greater knowledge about the state of the world, and increases the likelihood of a decision maker making the best decision. We discuss multi-dimensional game theory using linear programming methods to solve for multiple possible strategies. We discuss a new concept of reward volumes. The decision maker's success depends upon his ability to correctly and automatically judge the multiple factors. The goal of our system, called GlobalSite, is to deliver trustworthy threat analysis systems and services that understand situations, while being a vital tool for continuing mission operations.
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