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Knowledge-aided covariance matrix estimation and adaptive detection in compound-Gaussian noise

Bandiera, Francesco and Besson, Olivier and Ricci, Giuseppe Knowledge-aided covariance matrix estimation and adaptive detection in compound-Gaussian noise. (2010) IEEE Transactions on Signal Processing, vol. 58 (n° 10). pp. 5391-5396. ISSN 1053-587X

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Official URL: http://dx.doi.org/10.1109/TSP.2010.2052922

Abstract

We address the problem of adaptive detection of a signal of interest embedded in colored noise modeled in terms of a compound-Gaussian process. The covariance matrices of the primary and the secondary data share a common structure while having different power levels. A Bayesian approach is proposed here, where both the power levels and the structure are assumed to be random, with some appropriate distributions. Within this framework we propose MMSE and MAP estimators of the covariance structure and their application to adaptive detection using the NMF test statistic and an optimized GLRT herein derived. Some results, also conducted in comparison with existing algorithms, are presented to illustrate the performances of the proposed algorithms. The relevant result is that the solutions presented herein allows to improve the performance over conventional ones, especially in presence of a small number of training data.

Item Type:Article
Additional Information:Thanks to IEEE editor. 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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