Data Fusion Architectures

数据融合架构

• 批准号: 0701623
• 负责人: John Tsitsiklis
• 金额: $ 30万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-05-01 至 2011-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0701623&HistoricalAwards=false
• 关键词: Data; Fusion; Architectures

This research deals with sensor networks consisting of several sensors (nodes), each of which makes (generally noisy) observations related to a phenomenon of interest. The sensors use their observations, as well as messages received from other sensors, to form and transmit their own messages. The messages propagate through the network until, eventually one (e.g., a fusion center) or multiple sensors make a final decision. The broad objective is to address scalability issues and to derive important lessons on the meritsof different sensor network architectures, thus providing insight to a system designer as opposed to the narrower task of optimizing a given sensor networkIntellectual Merit:.The objective is to perform a detection task and decide between alternative hypotheses on the state of the environment, detect a target, based on noisy measurements, and in the face of severely limited (and possibly noisy) communications. These sensors may either communicate directly to a fusion center, or indirectly through a more general network topology. We will study the performance of different sensor topologies (trees, directed acyclic graphs, general graphs), and develop an understanding of the tradeoff betweenperformance and the architecture of the sensor network.Broader Impact:The broader impact of this work will be twofold. On the application side, data fusion andsensor networks play a prominent role in a vast range of contexts, from environmental monitoring to target recognition. However, very few guiding principles are currently available to engineers. New insights and architectural principles obtained will be valuable to system designers, resulting to reduced development time, and higher system efficiency. On the intellectual side, this work will provide a new paradigm for the elusive problem of designing "system architecture," yielding useful insights potentially transferable to broader classes of information processing systems. This project will contribute to education, human resource development, and training through teaching and mentoring graduate students, and through the dissemination of the results through articles, chapters, conferences, seminars, and short/tutorial courses.

本研究涉及由多个传感器（节点）组成的传感器网络，每个传感器（节点）都可以对感兴趣的现象进行（通常是有噪声的）观察。传感器使用它们的观察结果以及从其他传感器接收的消息来形成和传输它们自己的消息。消息通过网络传播，直到最终一个（例如，融合中心）或多个传感器做出最终决定。广泛的目标是解决可扩展性问题，并得出重要的教训的meritsof不同的传感器网络架构，从而提供洞察力的系统设计师，而不是优化给定的传感器networkIntellectual Merit的较窄的任务：。目标是执行检测任务，并决定在环境状态的替代假设之间，检测目标，噪声测量的基础上，并在面对严重有限的（可能是嘈杂的）通信。这些传感器可以直接与融合中心通信，也可以通过更一般的网络拓扑间接通信。我们将研究不同的传感器拓扑结构（树，有向无环图，一般图）的性能，并制定一个权衡betweenperformance和架构的传感器network.Broader影响的理解：这项工作的更广泛的影响将是双重的。在应用方面，数据融合和传感器网络在从环境监测到目标识别的广泛背景下发挥着重要作用。然而，目前很少有指导原则可供工程师使用。获得的新见解和体系结构原则对系统设计人员将是有价值的，从而减少开发时间，提高系统效率。在智力方面，这项工作将提供一个新的范式设计“系统架构”的难以捉摸的问题，产生有用的见解可能转移到更广泛的信息处理系统。该项目将通过教授和指导研究生，并通过文章、章节、会议、研讨会和短期/辅导课程传播成果，促进教育、人力资源开发和培训。