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Conference papers
Signal subspace change detection in structured covariance matrices
Abstract : Testing common properties between covariance matrices
is a relevant approach in a plethora of applications. In this
paper, we derive a new statistical test in the context of structured
covariance matrices. Specifically, we consider low rank signal
component plus white Gaussian noise structure. Our aim is to
test the equality of the principal subspace, i.e., subspace spanned
by the principal eigenvectors of a group of covariance matrices. A
decision statistic is derived using the generalized likelihood ratio
test. As the formulation of the proposed test implies a non-trivial
optimization problem, we derive an appropriate majorizationminimization
algorithm. Finally, numerical simulations illustrate
the properties of the newly proposed detector compared to the
state of the art.
Cited literature [20 references]
https://hal-univ-paris10.archives-ouvertes.fr/hal-02333861
Contributor : Administrateur Hal Nanterre <>
Submitted on : Friday, October 25, 2019 - 4:04:41 PM
Last modification on : Monday, February 10, 2020 - 6:14:15 PM
Document(s) archivé(s) le : Sunday, January 26, 2020 - 4:31:20 PM
Contributor : Administrateur Hal Nanterre <>
Submitted on : Friday, October 25, 2019 - 4:04:41 PM
Last modification on : Monday, February 10, 2020 - 6:14:15 PM
Document(s) archivé(s) le : Sunday, January 26, 2020 - 4:31:20 PM
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