DocumentCode :
3413137
Title :
Target detection and identification using canonical correlation analysis and subspace partitioning
Author :
Wang, Wei ; Adali, Tülay ; Emge, Darren
Author_Institution :
Dept. of CSEE, Maryland Univ.-Baltimore, Baltimore, MD
fYear :
2008
fDate :
March 31 2008-April 4 2008
Firstpage :
2117
Lastpage :
2120
Abstract :
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.
Keywords :
Raman spectra; correlation methods; set theory; signal detection; Raman spectroscopy; canonical correlation analysis; canonical vector; data-driven approach; linear mixture model; subspace partitioning; surface-deposited chemical agents; target detection; target identification; Biomedical signal processing; Chemicals; Contracts; Detectors; Least squares methods; Libraries; Object detection; Raman scattering; Spectroscopy; Testing; Raman spectroscopy; canonical correlation; identification; subspace partitioning; target detection;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on
Conference_Location :
Las Vegas, NV
ISSN :
1520-6149
Print_ISBN :
978-1-4244-1483-3
Electronic_ISBN :
1520-6149
Type :
conf
DOI :
10.1109/ICASSP.2008.4518060
Filename :
4518060
Link To Document :
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