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Knowledge-aided bayesian detection in heterogeneous environments

Besson, Olivier and Tourneret, Jean-Yves and Bidon, Stéphanie Knowledge-aided bayesian detection in heterogeneous environments. (2007) IEEE Signal Processing Letters, vol. 1 (n° 5). pp. 355-358. ISSN 1558-2361

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Official URL: http://dx.doi.org/10.1109/LSP.2006.888088

Abstract

We address the problem of detecting a signal of interest in the presence of noise with unknown covariance matrix, using a set of training samples. We consider a situation where the environment is not homogeneous, i.e., when the covariance matrices of the primary and the secondary data are different. A knowledge-aided Bayesian framework is proposed, where these covariance matrices are considered as random, and some information about the covariance matrix of the training samples is available. Within this framework, the maximum a priori (MAP) estimate of the primary data covariance matrix is derived. It is shown that it amounts to colored loading of the sample covariance matrix of the secondary data. The MAP estimate is in turn used to yield a Bayesian version of the adaptive matched filter. Numerical simulations illustrate the performance of this detector, and compare it with the conventional adaptive matched filter.

Item Type:Article
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution: Université de Toulouse > Université de Toulouse I-Sciences Sociales - UT1
Université de Toulouse > Institut Supérieur de l'Aéronautique et de l'Espace - ISAE
Université de Toulouse > Institut National Polytechnique de Toulouse - INPT
Université de Toulouse > Université Paul Sabatier-Toulouse III - UPS
French research institutions > Centre National de la Recherche Scientifique - CNRS
Université de Toulouse > Université de Toulouse II-Le Mirail - UTM
Laboratory name:
Statistics:download
Deposited By: Olivier Besson
Deposited On:20 May 2008 07:50

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