A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf
.
On the use of Empirical Likelihood for non-Gaussian clutter covariance matrix estimation
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
doi:10.1109/radar.2008.4720953
fatcat:5cco3o4ppzd5rcf7v3qsmqrvhu