Steady-state MSE performance analysis of mixture approaches to adaptive filtering

In this paper, we consider mixture approaches that adaptively combine outputs of several parallel running adaptive algorithms. These parallel units can be considered as diversity branches that can be exploited to improve the overall performance. We study various mixture structures where the final output is constructed as the weighted linear combination of the outputs of several constituent filters. Although the mixture structure is linear, the combination weights can be updated in a highly nonlinear manner to minimize the final estimation error such as in Singer and Feder 1999; Arenas-Garcia, Figueiras-Vidal, and Sayed 2006; Lopes, Satorius, and Sayed 2006; Bershad, Bermudez, and Tourneret 2008; and Silva and Nascimento 2008. We distinguish mixture approaches that are convex combinations (where the linear mixture weights are constrained to be nonnegative and sum up to one) [Singer and Feder 1999; Arenas-Garcia, Figueiras-Vidal, and Sayed 2006], affine combinations (where the linear mix...

• Type: Journal
• Year: 2010
• Where: TSP
• Authors: Suleyman Serdar Kozat, Alper T. Erdogan, Andrew C. Singer, Ali H. Sayed
• Keywords: Artificial Intelligence, Constituent Filters, Linear Mixture Weights, Mixture Structures, TSP 2010