Journal of Signal Processing Systems

, Volume 68, Issue 3, pp 379–390

A Novel Approach for Target Detection and Classification Using Canonical Correlation Analysis

Authors

    • Department of Computer Science and Electrical EngineeringUniversity of Maryland Baltimore County
  • Tülay Adalı
    • Department of Computer Science and Electrical EngineeringUniversity of Maryland Baltimore County
  • Darren Emge
    • US Army, Edgewood Chemical and Biological Center, Aberdeen Proving Grounds
Article

DOI: 10.1007/s11265-011-0625-7

Cite this article as:
Wang, W., Adalı, T. & Emge, D. J Sign Process Syst (2012) 68: 379. doi:10.1007/s11265-011-0625-7

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.

Keywords

Detection Classification Canonical correlation analysis

Copyright information

© Springer Science+Business Media, LLC 2011