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Adaptive detection of distributed targets in compound-Gaussian noise without secondary data: A Bayesian approach

Bandiera, Francesco and Besson, Olivier and Ricci, Giuseppe Adaptive detection of distributed targets in compound-Gaussian noise without secondary data: A Bayesian approach. (2011) IEEE Transactions on Signal Processing, vol. 59 (n°12). pp. 5698 -5708 . ISSN 1053-587X

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Official URL: http://dx.doi.org/10.1109/TSP.2011.2167613

Abstract

In this paper, we deal with the problem of adaptive detection of distributed targets embedded in colored noise modeled in terms of a compound-Gaussian process and without assuming that a set of secondary data is available.The covariance matrices of the data under test share a common structure while having different power levels. A Bayesian approach is proposed here, where the structure and possibly the power levels are assumed to be random, with appropriate distributions. Within this framework we propose GLRT-based and ad-hoc detectors. Some simulation studies are presented to illustrate the performances of the proposed algorithms. The analysis indicates that the Bayesian framework could be a viable means to alleviate the need for secondary data, a critical issue in heterogeneous scenarios.

Item Type:Article
Additional Information:Thanks to IEEE editor. (c) 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.The definitive version is available at http://ieeexplore.ieee.org
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution: Université de Toulouse > Institut Supérieur de l'Aéronautique et de l'Espace - ISAE
Other partners > Università del Salento (ITALY)
Laboratory name:
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Deposited By: Olivier Besson

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