Collaborative Research: ATD: Advanced Quickest Multidecision Change Detection-Classification Methods for Threat Assessment in Distributed Sensing Systems

合作研究：ATD：分布式传感系统中威胁评估的先进最快多决策变化检测分类方法

• 批准号: 1222498
• 负责人: Venugopal Veeravalli
• 金额: $ 25万
• 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: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1222498&HistoricalAwards=false
• 关键词: Collaborative; Research; ATD; Advanced; Quickest

The overarching goal of this project is to develop the next generation of mathematical and statistical algorithms and methodologies in sensor systems for the detection of chemical and biological materials based on advanced quickest change detection and classification methods. To this end, the next generation of the quickest joint change detection and classification methods will be developed that are optimal or nearly optimal in a variety of scenarios. Specifically, a general theory of multidecision quickest change detection and classification for non-i.i.d. stochastic models will be developed. Developing this general theory requires novel probabilistic methods for both designing effective quickest change detection-classification strategies as well as analyzing their performance. Furthermore, the general theory will be extended to the distributed sensor setting. In particular, novel techniques for adaptive sampling at the sensors will be explored, change process detection methods will be developed for settings where the change might occur at different times at the various sensors, and techniques for controlling the sensing process to make it energy-efficient will be designed. It is expected that the proposed theoretical advances in change detection and classification will have a strong practical impact on future systems that are built for the purposes of detecting and predicting chemical, biological and related threats using large sensor networks. Conversely the engineering insights gained from working on this important problem will lead to significant developments in the underlying statistical theory of quickest change detection and classification. Advances in this theory couldpotentially have an impact on a broad spectrum of applications from qualitycontrol engineering to econometrics.

该项目的总体目标是在先进的最快变化检测和分类方法的基础上，开发传感器系统中用于检测化学和生物材料的下一代数学和统计算法和方法。为此，将开发下一代最快的联合变化检测和分类方法，这些方法在各种情况下都是最佳或接近最佳的。具体地说，非I.I.D.的多决策最快变化检测和分类的一般理论。将开发随机模型。发展这一一般理论需要新的概率方法来设计有效的、最快的变化检测-分类策略以及分析其性能。此外，一般理论将扩展到分布式传感器环境。特别是，将探索在传感器处自适应采样的新技术，将针对在不同传感器处可能在不同时间发生变化的设置开发变化过程检测方法，并将设计用于控制传感过程以使其节能的技术。预计拟议的变化检测和分类方面的理论进步将对未来利用大型传感器网络检测和预测化学、生物和相关威胁的系统产生强大的实际影响。相反，从研究这一重要问题中获得的工程学见解将导致最快变化检测和分类的基本统计理论的重大发展。这一理论的进步可能会对从质量控制工程到计量经济学的广泛应用产生潜在影响。