Performance loss of multivariate detection algorithms due to covariance estimation

Charles E. Davidson; Avishai Ben-David

doi:10.1117/12.829988

28 September 2009 Performance loss of multivariate detection algorithms due to covariance estimation

Proceedings Volume 7477, Image and Signal Processing for Remote Sensing XV; 74770J (2009) https://doi.org/10.1117/12.829988
Event: SPIE Remote Sensing, 2009, Berlin, Germany

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.

Citation Download Citation

Charles E. Davidson and Avishai Ben-David "Performance loss of multivariate detection algorithms due to covariance estimation", Proc. SPIE 7477, Image and Signal Processing for Remote Sensing XV, 74770J (28 September 2009); https://doi.org/10.1117/12.829988

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