Advances in Engineering Advances in Engineering features breaking research judged by Advances in Engineering advisory team to be of key importance in the Engineering field. Papers are selected from over 10,000 published each week from most peer reviewed journals.
Wei Wang, Tulay Adalı, Darren Emge.
Journal of Signal Processing Systems, September 2012, Volume 68, Issue 3, pp 379-390
Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD, 21250, USA
US Army, Edgewood Chemical and Biological Center, Aberdeen Proving Grounds, Baltimore, MD, 21010, USA
Abstract
We present a novel detection approach, detection with canonical correlation (DCC), for target detection without prior information on the interference. We use the maximum canonical correlations between the target set and the observation data set as the detection statistic, and the coefficients of the canonical vector are used to determine the indices of components from a given target library, thus enabling both detection and classification of the target components that might be present in the mixture. We derive an approximate distribution of the maximum canonical correlation when targets are present. For applications where the contributions of components are non-negative, non-negativity constraints are incorporated into the canonical correlation analysis framework and a recursive algorithm is derived to obtain the solution. We demonstrate the effectiveness of DCC and its nonnegative variant by applying them on detection of surface-deposited chemical agents in Raman spectroscopy.
