Application of optimally distinguishable distributions to the detection of subspace signals in Gaussian noise of unknown level

Authors:
Seyed Alireza Razavi;Ciprian Doru Giurcneanu
Affiliations:
Department of Electronics and Communications Engineering, Tampere University of Technology, P.O. Box 553, FI-33101 Tampere, Finland;Department of Statistics, University of Auckland, Private Bag 92019, Auckland 1142, New Zealand
Venue:
Digital Signal Processing
Year:
2013

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Abstract

A detection method based on optimally distinguishable distributions (ODD) was introduced recently. However, ODD testing as it was originally formulated has an important limitation because it does not accommodate models with nuisance parameters. This paper demonstrates how the difficulty can be circumvented in the case of subspace signals in Gaussian noise of unknown level. The key point is to define a partition of the parameter space. To this end, we analyze two different methods, and we choose one of them as basis for the new ODD detector. The performance of the detector is compared with that of the GLRT (generalized likelihood ratio test). Additionally, we compute the confidence indexes which are part of the ODD methodology.