Journal of Signal Processing Systems

, Volume 68, Issue 3, pp 379–390 | Cite as

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

Article

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 

References

  1. 1.
    Kay, S. M. (1998). Fundamentals of statistical signal processing: Detection theory. Prentice Hall PTR, NJ.Google Scholar
  2. 2.
    Manolakis, D., Marden, D., & Shaw, G. A. (2003). Hyperspectral image processing for automatic target detection. MIT Lincoln Lab Journal, 14(1), 79–116.Google Scholar
  3. 3.
    ITT Industries (2003). Tests of the laser interrogation of surface agents system for on-the-move standoff sensing of chemical agents. In Proc. int. symp. spectral sensing research.Google Scholar
  4. 4.
    Scharf, L. L., & Friedlander, B. (1994). Matched subspace detectors. IEEE Transactions on Signal Processing, 42(8), 2146–2157.CrossRefGoogle Scholar
  5. 5.
    Manolakis, D., et al. (2001). Hyperspectral subpixel target detection using the linear mixing model. IEEE Transactions on Geoscience and Remote Sensing, 39(7), 1392–1409.CrossRefGoogle Scholar
  6. 6.
    Kraut, S., Scharf, L. L., & McWhorter, L. T. (2001). Adaptive subspace detectors. IEEE Transactions on Signal Processing, 49(1), 1–16.CrossRefGoogle Scholar
  7. 7.
    Wang, W., & Adalı, T. (2007). Detection using correlation bound in a linear mixture model. Signal Processing, 87(5), 1118–1127.MATHCrossRefGoogle Scholar
  8. 8.
    Wang, W., & Adalı, T. (2005). Constrained ICA and its application to Raman spectroscopy. In Proc. antennas and propagation society international symposium (pp. 109–112). Washington, DC.Google Scholar
  9. 9.
    Li, H., Adalı, T., Wang, W., & Emge, D. (2007). Non-negative matrix factorization with orthogonality constraints and its application to Raman spectroscopy. Journal of VLSI Signal Processing, 48, 83–97.CrossRefGoogle Scholar
  10. 10.
    Desai, M. N., & Mangoubi, R. S. (2003). Robust Gaussian and non-Gaussian matched subspace detection. IEEE Transactions on Signal Processing, 51(12).Google Scholar
  11. 11.
    Wang, W., Adalı, T., & Emge, D. (2007). Unsupervised detection using canonical correlation analysis and its application to Raman spectroscopy. In Proc. IEEE workshop on machine learning for signal processing, Thessaloniki, Greece.Google Scholar
  12. 12.
    Hotelling, H. (1936). Relations between two sets of variates. Biometrika, 28, 321–377.MATHGoogle Scholar
  13. 13.
    Anderson, T. W. (2003). An introduction to multivariate statistical analysis. CA: Wiley.MATHGoogle Scholar
  14. 14.
    Constantine, A. G. (1963). Some non-central distribution problems in multivariate analysis. Annals of Mathematical Statistics, 34(4), 1270–1285.MathSciNetMATHCrossRefGoogle Scholar
  15. 15.
    Hayakawa, T. (1967). On the distribution of the maximum latent root of a positive definite symmetric random matrix. Annals of the Institute of Statistical Mathematics, 21(1), 1–17.CrossRefGoogle Scholar
  16. 16.
    Fisher, R. A. (1928). The general sampling distribution of the multiple correlation coefficient. Proceedings of Royal Society, A, 121, 654–673.MATHCrossRefGoogle Scholar
  17. 17.
    Tenenhaus, M. (1988). Canonical analysis of two convex polyhedral cones and applications. Psychometrika, 53(4), 503–524.MathSciNetMATHCrossRefGoogle Scholar
  18. 18.
    Vía, J., Santamaría, I., & Pérez, J. (2005). A robust RLS algorithm for adaptive canonical correlation analysis. In Proc. IEEE int. conf. acoust., speech, signal processing, 4, 365–368. Philadelphia, PA.Google Scholar
  19. 19.
    Kennedy, W. J., & Gentle, J. E. (1980). Statistical computing. New York: Marcel Dekker.MATHGoogle Scholar
  20. 20.
    Slamani, M., Chyba, T., LaValley, H., & Emge, D. (2006). Identification algorithm for the joint contaminated surface detector (JCSD). In Proc. 2006 international symposium on spectral sensing research, Bar Harbor, ME.Google Scholar
  21. 21.
    Wang, W., Adalı, T., & Emge, D. (2009). Subspace partitioning for target detection and identification. IEEE Transactions on Signal Processing, 57(4), 1250–1259.MathSciNetCrossRefGoogle Scholar
  22. 22.
    Wang, W., & Adalı, T. (2008). Target detection and identification using canonical correlation and subspace partitioning. In Proc. IEEE int. conf. acoust., speech, signal processing (pp. 2117–2120). Las Vegas, NV.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Department of Computer Science and Electrical EngineeringUniversity of Maryland Baltimore CountyBaltimoreUSA
  2. 2.US Army, Edgewood Chemical and Biological Center, Aberdeen Proving GroundsBaltimoreUSA

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