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: 1514245
• 负责人: Venugopal Veeravalli
• 金额: $ 65.4万
• 依托单位: University of Illinois at Urbana-Champaign
• 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 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1514245&HistoricalAwards=false
• 关键词: CIF; Medium; Collaborative; Research; Quickest

The 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.

在随机系统中检测变化的问题，通常被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，环境中的异常以某种方式改变了顺序获得的观测值的分布。目标是检测变化并尽快发出警报，以便及时采取任何必要的行动，同时将假警报的比率控制在可接受的水平以下。虽然最快变化检测问题自20世纪50年代初以来一直在积极研究，但该领域存在许多理论和实践兴趣的开放挑战。本研究解决了快速变化检测中长期存在的开放性问题，以及由现代应用驱动的挑战性问题，例如：1)马尔可夫数据的最优快速变化检测；2)对瞬态变化的最优快速检测；3)稀疏变化时多流数据的最优（渐近）最快变化检测方案；4)联合多流数据中最快的变更检测和隔离（变更定位）；5)基于复合后变化假设的快速变化检测与隔离控制传感；6)数据驱动的最快离群值检测和隔离。此外，研究人员还研究了其成果在以下领域的应用：1)电力系统的线路中断检测；2)疫情检测；3)金融应用中的变化检测；4)传感器网络监控；5)动态频谱感知；6)电网/网络入侵检测；6)大数据异常与欺诈检测。