Signal subspace change detection in structured covariance matrices

Abstract

International audienceTesting common properties between covariance matricesis a relevant approach in a plethora of applications. In thispaper, we derive a new statistical test in the context of structuredcovariance matrices. Specifically, we consider low rank signalcomponent plus white Gaussian noise structure. Our aim is totest the equality of the principal subspace, i.e., subspace spannedby the principal eigenvectors of a group of covariance matrices. Adecision statistic is derived using the generalized likelihood ratiotest. As the formulation of the proposed test implies a non-trivialoptimization problem, we derive an appropriate majorizationminimizationalgorithm. Finally, numerical simulations illustratethe properties of the newly proposed detector compared to thestate of the art