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首页> 外文会议>Conference on Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2003 Apr 23-25, 2003 Orlando, Florida, USA >Sensor Management: Coverage versus Survivability - A Multiple Objective Optimization Problem
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Sensor Management: Coverage versus Survivability - A Multiple Objective Optimization Problem

机译：传感器管理：覆盖率与生存能力-多目标优化问题

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摘要

The Sensor Management Team from DSO National Laboratories has developed a Decision Support System (DSS) to assist human operators in determining the most effective employment and/or deployment of a suite of sensors given a particular mission or operational scenario. The key issue addressed by the system is the resource allocation problem accompanied by two contradictory objectives, namely to maximise combined coverage of the sensor suite and to maximise survivability of the sensor within the suite. Furthermore, the system is to handle operational constraints on the usage of the suite of sensors. In this paper, we will describe how we handle the problem by formulating it as a Multiple Objective Optimization (MOO) problem. This system may be used as a pre-mission planning tool or a real time decision aid for the sensor suite commander. With the increase in size of the sensor suite and the number of possible deployment sites, the feasibility space of the employment/deployment configurations will grow tremendously. In order to allow for near real time decision support, the team has incorporated genetic algorithm to solve the MOO problem.
机译：DSO国家实验室的传感器管理团队已经开发了决策支持系统（DSS），以帮助人类操作人员确定在特定任务或操作场景下最有效地使用和/或部署整套传感器套件。该系统解决的关键问题是伴随着两个矛盾目标的资源分配问题，即最大化传感器套件的组合覆盖范围和最大化传感器在套件中的生存能力。此外，该系统将处理传感器套件使用方面的操作限制。在本文中，我们将通过将其表述为多目标优化（MOO）问题来描述如何处理该问题。该系统可用作传感器套件指挥官的任务前计划工具或实时决策辅助工具。随着传感器套件尺寸的增加和可能的部署地点的数量，使用/部署配置的可行性空间将大大增加。为了提供近乎实时的决策支持，该团队采用了遗传算法来解决MOO问题。

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