Covariance-informed detection in compound-Gaussian clutter without secondary data

Abstract

We consider the problem of detecting a signal of interest in the presence of compound-Gaussian clutter, without resorting to secondary data in order to infer the clutter covariance matrix. Towards this end, we assume that both the texture τ and the speckle covariance matrix R are random variables with some a priori distributions. Marginalizing with respect to these variables, the probability density function of the observed primary data is derived, leading to a closed-form expression for the generalized likelihood ratio test (GLRT) of the problem at hand. Accordingly, the GLRT assuming that τ is deterministic is also derived. The two detectors are assessed through numerical simulations


Tutti gli autori

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

Titolo volume/Rivista

Non Disponibile


Anno di pubblicazione

2010

ISSN

Non Disponibile

ISBN

Non Disponibile


Numero di citazioni Wos

Nessuna citazione

Ultimo Aggiornamento Citazioni

Non Disponibile


Numero di citazioni Scopus

2

Ultimo Aggiornamento Citazioni

28/04/2018


Settori ERC

Non Disponibile

Codici ASJC

Non Disponibile