Abstract:
This paper derives fundamental results for data validation in the presence of imprecisely known correlations. Given a constraint on the maximum absolute correlation of a ...Show MoreMetadata
Abstract:
This paper derives fundamental results for data validation in the presence of imprecisely known correlations. Given a constraint on the maximum absolute correlation of a given estimate and measurement data, a tight upper bound for the joint covariance matrix is derived, which finally yields a modified Mahalanobis distance. The special cases of one-dimensional and two-dimensional random variables are discussed.
Published in: 2003 European Control Conference (ECC)
Date of Conference: 01-04 September 2003
Date Added to IEEE Xplore: 23 April 2015
Print ISBN:978-3-9524173-7-9
Inst. of Computer Design and Fault Tolerance, Universität Karlsruhe, Karlsruhe, Germany
Siemens AG, Information and Comm., München, Germany
Contents Inst. of Computer Design and Fault Tolerance, Universität Karlsruhe, Karlsruhe, Germany
Siemens AG, Information and Comm., München, Germany
