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

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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.

Autore Pugliese

Tutti gli autori

  • F. Bandiera , O. Besson , G. Ricci

Titolo volume/Rivista

IEEE TRANSACTIONS ON SIGNAL PROCESSING

Anno di pubblicazione

2011

ISSN

1053-587X

ISBN

Non Disponibile

Numero di citazioni Wos

26

Ultimo Aggiornamento Citazioni

28/04/2018

Numero di citazioni Scopus

40

Ultimo Aggiornamento Citazioni

28/04/2018

Settori ERC

Non Disponibile

Codici ASJC

Non Disponibile