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.
