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Adaptive Detection of Distributed Targets in Compound-Gaussian Noise Without Secondary Data: A Bayesian Approach

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3 Author(s)
Bandiera, F. ; Dipt. di Ing. dell''Innovazione, Univ. del Salento, Lecce, Italy ; Besson, O. ; Ricci, G.

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.

Published in:

Signal Processing, IEEE Transactions on  (Volume:59 ,  Issue: 12 )

Date of Publication:

Dec. 2011

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