On the use of Empirical Likelihood for non-Gaussian clutter covariance matrix estimation

Hugo Harari-Kermadec, Frederic Pascal
2008 2008 IEEE Radar Conference  
This paper presents a improved estimation scheme when the clutter distribution is unknown. The Empirical Likelihood (EL) is a recent semi-parametric estimation method [11] which allows to estimate unknown parameters by using information contained in the observed data such as constraints on the parameter of interest as well as an a priori structure. The aim of this paper is twofold. First, the empirical likelihood is briefly introduced and then, some constraints on the unknown parameters are
more » ... parameters are added. To illustrate this situation, we focus on the problem of estimating the clutter covariance matrix when this matrix is assumed to be Toeplitz [4], [7] . Finally, theoretical results are emphasized by several simulations corresponding to real situations: the mixture of a Gaussian (thermal noise) and a non-Gaussian (clutter) noise.
doi:10.1109/radar.2008.4720953 fatcat:5cco3o4ppzd5rcf7v3qsmqrvhu