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

Authors:
Wei Wang;Tülay Adalı;Darren Emge
Affiliations:
Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, USA 21250;Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, USA 21250;US Army, Edgewood Chemical and Biological Center, Aberdeen Proving Grounds, Baltimore, USA 21010
Venue:
Journal of Signal Processing Systems
Year:
2012

Citing 7
Cited 0

Fundamentals of statistical signal processing: estimation theory

Fundamentals of statistical signal processing: estimation theory
Detection using correlation bound in a linear mixture model

Signal Processing
Non-negative Matrix Factorization with Orthogonality Constraints and its Application to Raman Spectroscopy

Journal of VLSI Signal Processing Systems
Subspace partitioning for target detection and identification

IEEE Transactions on Signal Processing
Robust Gaussian and non-Gaussian matched subspace detection

IEEE Transactions on Signal Processing
Adaptive subspace detectors

IEEE Transactions on Signal Processing
Matched subspace detectors

IEEE Transactions on Signal Processing

Quantified Score

Hi-index	0.00

Visualization

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.