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

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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.