CIF: Medium Projects: Event-Triggered Sampling: Application to Decentralized Detection and Estimation

对连续时间信号进行采样是现代数字技术时代的一个基本过程。研究了一类新颖的事件触发采样器，其采样时间由被采样的实际信号动态决定。事件触发采样方案的丰富性允许对有意义的优化问题进行数学表述，这些优化问题可以导致具有最佳特性的采样策略，并可能导致显著的性能提升。本课题所研究的理论和技术将对基于传感器网络的事件监测和监测、基于智能电网的发电和配电网络控制系统等重要应用领域具有重要意义。本文主要研究分散统计信号处理中的三个主要问题，即序贯检测(假设检验和变点检测)、参数估计和信号估计与控制。在所有这三个问题中，将研究最优的事件触发机制，以优化相应的检测或估计性能。此外，还将分析一类特殊的事件触发采样器，并开发相应的数字信号处理(DSP)设计方法，该研究将对统计信号处理和数字信号处理领域产生革命性的影响。特别是，在检测、估计和控制中使用事件触发采样可以显著提高分散设置下的性能，并在功率和带宽方面显著简化通信要求。

Sampling continuous-time signals constitutes a fundamental process in the modern digital technology era. This research investigates a novel class of event-triggered samplers, in which the sampling times are dictated in a dynamic way by the actual signals to be sampled. The richness of the event-triggered sampling schemes allows for the mathematical formulation of meaningful optimization problems that can lead to sampling strategies with optimal characteristics and resulting in potentially significant performance gains. The theories and techniques to be developed in this project can potentially make significant contributions to important application areas such as sensor network systems for event surveillance and monitoring, and networked control systems for smart-grid-based power generation and distribution.This research focuses on three major problems in the general areas of decentralized statistical signal processing, namely, sequential detection (hypothesis testing and changepoint detection), parameter estimation, and signal estimation and control. In all three problems, optimal event-triggered mechanisms will be investigated that can optimize the corresponding detection or estimation performance. In addition, a special class of event-triggered samplers will be analyzed and the corresponding design methods for digital signal processing (DSP) will be developed .This research will have transformative impacts on the fields of statistical signal processing and digital signal processing. In particular, the use of event-triggered sampling in detection, estimation and control can significantly improve the performance in a decentralized setup and simplify considerably the communication requirements in terms of power and bandwidth.