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ICASSP
2008
IEEE

Target detection and identification using canonical correlation analysis and subspace partitioning

8 years 10 months ago
Target detection and identification using canonical correlation analysis and subspace partitioning
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.
Wei Wang 0018, Tülay Adali, Darren Emge
Added 30 May 2010
Updated 30 May 2010
Type Conference
Year 2008
Where ICASSP
Authors Wei Wang 0018, Tülay Adali, Darren Emge
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