A Novel Approach for Target Detection and Classification Using Canonical Correlation Analysis
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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.
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- A Novel Approach for Target Detection and Classification Using Canonical Correlation Analysis
Journal of Signal Processing Systems
Volume 68, Issue 3 , pp 379-390
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- Springer US
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- Canonical correlation analysis
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- Author Affiliations
- 1. Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD, 21250, USA
- 2. US Army, Edgewood Chemical and Biological Center, Aberdeen Proving Grounds, Baltimore, MD, 21010, USA