Expected-likelihood covariance matrix estimation for adaptive detection

Abstract

We demonstrate that by adopting the new class of "expected-likelihood" (EL) covariance matrix estimates, instead of the traditional maximum-likelihood (ML) estimates, we can significantly enhance adaptive detection performance. These new estimates are found by searching within the properly parameterized class of admissible covariance matrices for the one that produces the likelihood ratio (LR) that is "closest possible" to the LR generated by the true (exact) covariance matrix.