Signal Processing 2019 DOI: 10.1016/j.sigpro.2019.06.030 View full text
Bruno Mériaux, Chengfang Ren, Mohammed Nabil El Korso, Arnaud Breloy, Philippe Forster

Abstract: Covariance matrix estimation is a ubiquitous problem in signal processing. In most modern signal processing applications, data are generally modeled by non-Gaussian distributions with covariance matrices exhibiting a particular structure. Taking into account this structure and the non-Gaussian behavior improve drastically the estimation accuracy. In this paper, we consider the estimation of structured scatter matrix for complex elliptically distributed observations, where the assumed model can differ from the …

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