Recursive estimation of the scatter matrix of ECD: the Riemannian Information Gradient method

Abstract

ECD (elliptically-contoured distribution) models have been found remarkably successful in representing natural signals. At present, the estimation of these models is at the heart of numerous signal processing applications. Unfortunately, state-of-the-art methods for estimating the parameters of an ECD, especially its scatter matrix, may turn out to have excessive computational complexity. To remedy this problem, the present work introduces the Riemannian information gradient method, for recursive (i.e. online) estimation of the scatter matrix. It is shown that this method holds a significant advantage in terms of computational complexity, while still achieving the same performance as state-of-the-art methods.