ATD: Mathematical Algorithms for Characterizing Spectral Signatures of Chemical and Biological Agents

ATD：表征化学和生物制剂光谱特征的数学算法

• 批准号: 0915262
• 负责人: Michael Kirby
• 金额: $ 40.17万
• 依托单位: Colorado State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0915262&HistoricalAwards=false
• 关键词: ATD; Mathematical; Algorithms; Characterizing; Spectral

This proposal concerns the development and evaluation of new mathematical algorithms for the detection and classification of chemical and biological agents. An automatic detection and classification system operates by first identifying the existence of a signal of interest followed by classification of the signal. The classification stage consists of comparing the observation of interest to a small library of spectra of materials of interest. There are a host of significant challenges, both logistical and technical, surrounding such automatic detection and classification of threat agents in the field. The actual spectra collected in the field may have signatures which are not a direct match to the Raman spectrum collected under controlled conditions and the amplitude of the signal of interest is generally much smaller than the amplitude of the continuously changing background spectra. New mathematical algorithms will be developed for characterizing spectral signatures using an integrated geometric and statistical approach.The results of this research program are intended to be employed in mobile field-operable systems for the laser interrogation of surface agents (LISA) to enabled real time sensing and characterization of potential civilian and military exposure biological and chemical agents. As an example, a portable Mini-Raman Lidar System (MRLS) has been developed capable of measuring Raman spectral signatures at short standoff distances, e.g., 1-2.5 meters. These systems may be mounted on vehicles and could be used to support military operations by detecting toxic fingerprints and alerting military personnel to potential threats from chemical or biological weapons. This new technology has created the need to produce an automated biological and chemical threat agent detection system based on exploiting the characteristic signatures of Raman spectra associated with different compounds including warfare agents. The main objective of the proposed investigation is to develop algorithms capable of agent identification witha false positive rate of less than one in 90,000. This project will train two graduate students in an area of mathematics that has applications to National Security.

该提案涉及开发和评价用于化学和生物制剂检测和分类的新数学算法。自动检测和分类系统通过首先识别感兴趣的信号的存在，然后对信号进行分类来操作。分类阶段包括将感兴趣的观察结果与感兴趣的材料的小光谱库进行比较。围绕着这种对实地威胁物剂的自动检测和分类，存在着许多重大的后勤和技术挑战。在现场收集的实际光谱可能具有与在受控条件下收集的拉曼光谱不直接匹配的特征，并且感兴趣的信号的幅度通常比连续变化的背景光谱的幅度小得多。将开发新的数学算法，利用综合几何和统计方法来表征光谱特征，这一研究方案的结果将用于移动的现场可操作系统，用于表面剂的激光询问（丽莎），以实现真实的时间感测和表征潜在的民用和军用接触生物和化学剂。作为一个例子，已经开发了便携式微型拉曼激光雷达系统（MRLS），其能够在短的间隔距离处测量拉曼光谱特征，例如，1-2.5米。这些系统可以安装在车辆上，通过检测有毒指纹和提醒军事人员注意化学或生物武器的潜在威胁来支持军事行动。这种新技术产生了生产自动化生物和化学威胁剂检测系统的需要，该系统基于利用与包括战剂的不同化合物相关的拉曼光谱的特征签名。拟议的调查的主要目标是开发能够识别代理的算法，其假阳性率小于1/90，000。 该项目将培养两名研究生在数学领域，有应用到国家安全。