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Proceedings Paper

Performance loss of multivariate detection algorithms due to covariance estimation
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Paper Abstract

Performance of the matched filter and anomaly detection algorithms relies on the quality of the inverse sample covariance matrix, which depends on sample size (number of vectors). The "RMB rule" provides the number of vectors required to achieve a specific average performance loss of the matched filter. In this paper we extend the RMB rule to provide the number of vectors needed to ensure a minimum performance loss (within a certain confidence). We also review a general metric for covariance estimation accuracy based on the Wishart distribution and discuss anomaly detector performance loss.

Paper Details

Date Published: 28 September 2009
PDF: 12 pages
Proc. SPIE 7477, Image and Signal Processing for Remote Sensing XV, 74770J (28 September 2009); doi: 10.1117/12.829988
Show Author Affiliations
Charles E. Davidson, Science and Technology Corp. (United States)
Avishai Ben-David, U.S. Army Edgewood Chemical Biological Ctr. (United States)


Published in SPIE Proceedings Vol. 7477:
Image and Signal Processing for Remote Sensing XV
Lorenzo Bruzzone; Claudia Notarnicola; Francesco Posa, Editor(s)

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