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A novel method of constructing a joint probability density function (PDF) under H(sub 1), when the joint PDF under H(sub 0) is known, is developed. It has direct application in distributed detection systems. The construction is based on the exponential family, and it is shown that asymptotically the constructed PDF is optimal. The generalized likelihood ratio test (GLRT) is derived based on this method for the partially observed linear model. Interestingly, the test statistic is equivalent to the clairvoyant GLRT, which uses the true PDF under H(sub 1), even if the noise is nonGaussian., Presented at the 13th International Conference on Information Fusion held in Edinburgh, United Kingdom, on 26-29 July 2010. The conference was sponsored in part by the Office of Naval Research, Office of Naval Research Global, and the U.S. Army Research Laboratory's Army Research Office (ARO). This work was sponsored in part by the Air Force Research Laboratory, Sensors Directorate, and by the Edgewood Chemical Biological Center, Aberdeen Proving Ground, MD.

Raman spectroscopy is a powerful and effective technique for analyzing and identifying the chemical composition of a substance. Two types of Raman spectra estimation algorithms exist: supervised and unsupervised. In this paper, we perform a comparative analysis of five supervised algorithms for estimating Raman spectra. We describe a realistic measurement model for a dispersive Raman measurement device and observe that the measurement error variances vary significantly with bin index. Monte Carlo analyses with simulated measurements are used to calculate the bias root mean square error, and computational time for each algorithm. Our analyses show that it is important to use correct measurement weights and enforce the nonnegative constraint in parameter estimation., See also ADM002299. Presented at the International Conference on Information Fusion (12th) (Fusion 2009). Held in Seattle, Washington, on 6-9 July 2009. U.S. Government or Federal Rights License.. The original document contains color images.

We present a data-driven approach for target detection and identification based on a linear mixture model. Our aim is to determine the existence of certain targets in a mixture without specific information on the targets or the background, and to identify the targets from a given library. We use the maximum canonical correlation between the target set and the observations as the detection score, and use coefficients of the canonical vector to identify the indices of the present components from the given target library. The performance of the detector is enhanced using subspace partitioning on the target library. Both simulation and experimental results are presented to demonstrate the effectiveness of the proposed method in Raman spectroscopy for detection of surface-deposited chemical agents., See also ADM002091. Presented at the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2008) held in Las Vegas, Nevada on March 30-April 4, 2008. Published in the Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2008) p2117-2120, 2008. The original document contains color images.

A number of recent studies by this group and others have demonstrated the ability of normal Raman and surface-enhanced Raman spectroscopy (SERS) to identify bacteria at the species level. Our efforts have focused on the use of colloidal silver as the SERS active substrate. The addition of silver nanoparticles to the bacteria not only produces an enhanced Raman signal, but it also suppresses the native biofluorescence associated with visible laser excitation. Raman chemical imaging uses every pixel or a binned pixel group of the Raman camera as an independent Raman spectrograph. Thus, spatially resolved Raman spectral information is obtained; much like a visual microscope provides spatially resolved visual information. The advantage of this technique in biological detection resides primarily in analysis of biological samples in complex backgrounds without the need for any sample pre-processing. Using a chemical imaging Raman microscope, we compare normal Raman chemical imaging to SERS chemical imaging of a complex mixture of bacteria. In both cases we are able to differentiate single bacteria in the Raman microscope's field of view, but with a substantial reduction in analysis time for SERS chemical imaging., See also ADM002187. Presented at the Army Science Conference (26th) held in Orlando, Florida on 1-4 December 2008, Published in Proceedings of the Army Science Conference (26th), p1-5, Dec 2008. The original document contains color images.