Université Paris-Saclay (Espace Technologique, Bat. Discovery - RD 128 - 2e ét., 91190 Saint-Aubin - France)
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.
https://hal-univ-paris10.archives-ouvertes.fr/hal-02333861
Contributor : Administrateur Hal Nanterre
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Submitted on : Friday, October 25, 2019 - 4:04:41 PM
Last modification on : Friday, November 15, 2019 - 10:14:32 PM
R. Ben Abdallah, A. Breloy, A. Taylor, M. N. El Korso, David Lautru. Signal subspace change detection in structured covariance matrices. 27th European Signal Processing Conference, Sep 2019, Coruna, Spain. ⟨hal-02333861⟩
