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

该项目的总体目标是在基于先进的最快变化检测和分类方法的基础上，开发用于检测化学和生物材料的传感器系统中的下一代数学和统计算法和方法。为此，下一代最快的联合变化检测和分类方法将被开发出来，这些方法在各种场景下都是最优或接近最优的。具体而言，本文提出了一种多决策快速变化检测与分类的一般理论。将开发随机模型。发展这一一般理论需要新颖的概率方法来设计有效的快速变化检测-分类策略以及分析其性能。进一步，将一般理论推广到分布式传感器的设置。特别是，将探索传感器自适应采样的新技术，将开发变化过程检测方法，用于可能在不同时间发生变化的各种传感器的设置，并设计控制传感过程以使其节能的技术。预计在变化检测和分类方面提出的理论进展将对未来为使用大型传感器网络检测和预测化学、生物和相关威胁而建立的系统产生强烈的实际影响。相反，从研究这个重要问题中获得的工程见解将导致最快变化检测和分类的基础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影响。

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.