CIF: Medium: Collaborative Research: Quickest Change Detection Techniques with Signal Processing Applications

CIF：媒介：协作研究：信号处理应用的最快变化检测技术

• 批准号: 1513373
• 负责人: Dimitris Metaxas
• 金额: $ 46.72万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1513373&HistoricalAwards=false
• 关键词: CIF; Medium; Collaborative; Research; Quickest

CIF: Medium: Collaborative Research: Quickest Change Detection Techniques with Signal Processing ApplicationsProject abstractThe problem of detecting changes in stochastic systems, often referred to as sequential change detection or quickest change detection, arises in various branches of science and engineering. In all these applications, an anomaly in the environment changes in some way the distribution of the sequentially acquired observations. The goal is to detect the change and raise an alarm as soon as possible, so that any necessary action can be taken in time, while controlling the rate of false alarms below an acceptable level. While the quickest change detection problem has been actively studied since early 1950s, there are many open challenges in this field that are of theoretical as well as practical interest. This research addresses long-standing open problems in quickest change detection, as well as challenging problems that are motivated by modern applications, such as the following: 1) Optimum quickest change detection for Markov data; 2) Optimum quickest detection for transient changes; 3) (Asymptotically) optimum quickest change detection schemes for multistream data when changes are sparse; 4) Joint quickest change detection and isolation (localization of the change) in multistream data; 5) Controlled sensing for quickest change detection and isolation with composite post change hypothesis; 6) Data-driven quickest outlier detection and isolation. Furthermore, the investigators study the applications of their results in the following areas: 1) Line outage detection in power systems; 2) Epidemic detection; 3) Change detection in financial applications; 4) Surveillance using sensor networks; 5) Dynamic spectrum sensing; 6) Intrusion detection in power grids/networks; 6) Anomaly and fraud detection in big data.

CIF：中：协作研究：信号处理应用的最快变化检测技术项目摘要检测随机系统中的变化的问题，通常称为顺序变化检测或最快变化检测，出现在科学和工程的各个分支中。在所有这些应用中，环境中的异常会以某种方式改变顺序获取的观测值的分布。目标是尽快检测到变化并发出警报，以便及时采取任何必要的行动，同时将误报率控制在可接受的水平以下。虽然自 20 世纪 50 年代初以来人们一直在积极研究最快变化检测问题，但该领域仍存在许多具有理论和实际意义的开放挑战。这项研究解决了最快变化检测中长期存在的开放性问题，以及现代应用程序引发的挑战性问题，例如：1）马尔可夫数据的最佳最快变化检测； 2) 瞬时变化的最佳最快检测； 3）当变化稀疏时，（渐近）多流数据的最佳最快变化检测方案； 4）多流数据中联合最快的变化检测和隔离（变化的本地化）； 5) 受控传感，通过复合变化后假设实现最快的变化检测和隔离； 6) 数据驱动的最快异常值检测和隔离。此外，研究人员还研究了其结果在以下领域的应用：1）电力系统中的线路停电检测； 2）疫情检测； 3）金融应用中的变更检测； 4）利用传感器网络进行监控； 5）动态频谱感知； 6）电网/网络的入侵检测； 6）大数据中的异常和欺诈检测。