Target detection and identification using canonical correlation analysis and subspace partitioning

被引：2

作者：

Wang, Wei ^{[1
]}

Adali, Tuelay ^{[1
]}

Emge, Darren ^{[2
]}

机构：

[1] Univ Maryland, Dept CSEE, 1000 Hilltop Circle, Baltimore, MD 21250 USA

[2] US Army, Edgewood Chem & Biol Ctr, Aberdeen Proving Ground, MD 21010 USA

来源：

2008 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING, VOLS 1-12 | 2008年

关键词：

target detection; identification; canonical correlation; subspace partitioning; Raman spectroscopy;

D O I：

10.1109/ICASSP.2008.4518060

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

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.

引用

页码：2117 / +

页数：2