Intelligent Distributed Estimation Architectures

智能设备和日常用品配备了多个传感器-今天已经-生成大量的传感器数据，这些数据需要过滤，分析，监控和处理。为了克服这一挑战，丰富的传感器数据通常被划分为子集，并送入并行运行的多个过滤器。这种方法可以被理解为自底向上的方法，它依赖于多个独立的局部滤波器的设计和运行，收集估计结果，并将它们融合到全局估计中。由于每个局部滤波器都是单独设计的，并且不知道全局估计目标，因此自底向上的方法通常不能产生最优的和计算效率高的全局estimate.This项目从不同的角度来看待大规模分布式估计问题的解决方案，主要关注所需的全局估计质量。该方法从全局估计质量的要求出发，设计局部滤波器的结构和拓扑，以满足全局估计质量的要求，同时尽可能降低计算复杂度。考虑到分布式计算架构的最新发展，自上而下的方法尤为重要，分布式计算架构为用户和设计人员提供了巨大的灵活性和可变性，以实现不同的和可能随时间变化的估计目标。该项目将是一种新颖的尝试，由于其积极主动的方法来改变相对于给定的全球估计目标的估计架构。使用这种方法，可以建议对架构的适应，而无需对给定架构进行广泛的试验。为此，该项目将分析估计架构的构建模块，即数据流、网络拓扑、融合策略和滤波器参数，重点关注它们对全局目标的单独和合作影响。架构内的处理单元还具有监控和故障检测功能，以使架构适应不断变化的操作条件和要求。我们认为这是物联网时代的一个及时考虑。

Smart devices and everyday items equipped with a plurality of sensors - already today - generate massive amounts of sensor data that are to be filtered, analyzed, monitored, and processed. To overcome this challenge, abundant sensor data are typically partitioned into subsets and fed into multiple filters running in parallel. Such an approach, which can be understood as the bottom-up approach, relies on a design and run of multiple independent local filters, collecting the estimation results, and fusing them into a global estimate. As each local filter is designed separately and is not aware of the global estimation goal, the bottom-up approach cannot generally result in an optimal and computationally efficient global estimate.This project views the solution to the large-scale distributed estimation problem from a different perspective by primarily focusing on the desired global estimation quality. The proposed top-down approach starts from the requirements on the global estimation quality and designs the structure and topology of local filters to meet the requirements while the computational complexity is kept as low as possible. The top-down approach is especially important considering the recent developments in distributed computing architectures which give an enormous flexibility and variability to the users and designers to achieve different and possibly time-varying estimation goals.The project will be a novel attempt due to its proactive approach to change the estimation architecture with respect to a given global estimation aim. With such an approach, it is possible to suggest an adaptation to the architecture without doing extensive trials with a given architecture. For this aim, the project will analyze the building blocks of the estimation architecture; that is the data flow, network topology, fusion strategy, and filter parameters with a focus on their individual and cooperative impact on the global objective. The processing units within the architecture also feature monitoring and fault detection functionalities in order to adapt the architecture to changing operational conditions and requirements. We believe this to be a timely consideration in the age of the Internet of Things.