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

CIF：中型项目：事件触发采样：在去中心化检测和估计中的应用

基本信息

批准号：
1064575
负责人：
Xiaodong Wang
金额：
$ 95.27万
依托单位：
Columbia University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2011
资助国家：
美国
起止时间：
2011-07-01 至 2017-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1064575&HistoricalAwards=false
关键词：
CIF Medium Projects Event Triggered

项目摘要

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.

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。这项研究研究了一类新颖的事件触发采样器，其中采样时间由要采样的实际信号以动态方式决定。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公式化，从而产生具有最佳特性的采样策略，并带来潜在的显着性能提升。该项目将开发的理论和技术可能对事件监视和监测的传感器网络系统以及基于智能电网的发电和配电的网络控制系统等重要应用领域做出重大贡献。本研究重点关注分散统计信号处理一般领域的三个主要问题，即顺序检测（假设检验和变点检测）、参数估计以及信号估计和控制。在所有三个问题中，将研究最佳事件触发机制，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。这项研究将对统计信号处理和数字信号处理领域产生变革性影响。特别是，在检测、估计和控制中使用事件触发采样可以显着提高分散设置的性能，并大大简化功率和带宽方面的通信要求。