Data Fusion Architectures

这项研究涉及由几个传感器(节点)组成的传感器网络，每个传感器(节点)进行与感兴趣的现象相关的(通常有噪声的)观测。传感器使用它们的观测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伸缩性问题，并从不同传感器网络体系结构的优点中获得重要的经验教训，从而为系统设计者提供洞察力，而不是更狭隘的任务优化给定的传感器网络的智能优点：目标是执行检测任务并在环境状态的可选假设之间做出决定，基于噪声测量来检测目标，以及面对严重受限(和可能是噪声)的通信。这些传感器可以直接与融合中心通信，也可以通过更通用的网络拓扑间接通信。我们将研究不同传感器拓扑(树、有向无环图、一般图)的性能，并了解传感器网络的性能和体系结构之间的权衡。更广泛的影响：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环境中发挥着突出的作用。然而，目前工程师可以使用的指导原则很少。获得的新见解和体系结构原则将对系统设计人员有价值，从而减少开发时间和提高系统效率。在智力方面，这项工作将为设计“系统架构”这一难以捉摸的问题提供一个新的范例，产生有用的见解，有可能转移到更广泛的信息处理系统类别。该项目将通过教授和指导研究生，以及通过文章、章节、会议、研讨会和短期/辅导性课程传播成果，为教育、人力资源开发和培训作出贡献。

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.