Covariance Matrix Estimation in SIRV and Elliptical Processes and Their Applications in Radar Detection

Abstract : As developed in this chapter, the detection performances are strongly linked to the covariance matrix estimation process. Several estimation methods have been studied through the statistical properties of the estimators. Then, they have been used in various detection problems on simulated data and real datasets. These results have enlightened the interest of using advanced estimation methods. Notice that there was not an exhaustive presentation of the different covariance matrix estimation approaches. Recently, to tackle the problem of few secondary data, as well as to deal with robustness matters, improved regularization techniques have been introduced [71-74]. One can also mentioned the promising framework of the Random Matrix Theory with some recent results in robust covariance matrix estimation for signal processing applications.
Type de document :
Chapitre d'ouvrage
IET. Modern Radar Detection Theory, Scitech Publishing, pp.295-332, 2015, 978-1-61353-199-0. 〈10.1049/SBRA509E_ch〉
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Contributeur : Virginie Bouvier <>
Soumis le : vendredi 26 février 2016 - 14:17:50
Dernière modification le : jeudi 13 septembre 2018 - 15:24:04

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Jean-Philippe Ovarlez, Frédéric Pascal, Philippe Forster. Covariance Matrix Estimation in SIRV and Elliptical Processes and Their Applications in Radar Detection. IET. Modern Radar Detection Theory, Scitech Publishing, pp.295-332, 2015, 978-1-61353-199-0. 〈10.1049/SBRA509E_ch〉. 〈hal-01279530〉

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