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

Jean-Philippe Ovarlez (1, 2) , Frédéric Pascal (3) , Philippe Forster (4)

1 ONERA - The French Aerospace Lab [Palaiseau]
2 SONDRA - Sondra, CentraleSupélec, Université Paris-Saclay (COmUE)
3 L2S - Laboratoire des signaux et systèmes
4 SATIE - Systèmes et Applications des Technologies de l'Information et de l'Energie

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.

Domains: Engineering Sciences [physics]; Engineering Sciences [physics]; Signal and Image processing

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, chapter 8, Scitech Publishing, pp.295-332, 2015, 978-1-61353-199-0. ⟨10.1049/SBRA509E_ch⟩. ⟨hal-01279530⟩