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

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连续获得的观测值的分布。目标是尽快发现变化并发出警报，以便及时采取任何必要的行动，同时将误警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年代初以来一直被积极研究，但该领域仍存在许多具有理论和实践意义的开放挑战。这项研究解决了最快变化检测中的长期悬而未决的问题，以及现代应用所激发的挑战性问题，例如：1)马尔可夫数据的最快变化检测；2)瞬时变化的最快检测；3)当变化稀疏时，(渐近)多数据流数据的最快变化检测方案；4)多数据流数据中的联合最快变化检测和隔离(变化的定位)；5)基于复合变化后假设的受控感知最快变化检测和隔离；6)数据驱动的最快离群点检测和隔离。此外，研究人员还研究了他们的结果在以下领域的应用：1)电力系统中的线路中断检测；2)流行病检测；3)金融应用中的变化检测；4)传感器网络的监控；5)动态频谱感知；6)电网/网络中的入侵检测；6)大数据中的异常和欺诈检测。

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.