Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
564013 | Signal Processing | 2013 | 12 Pages |
Because of the high computational burden required by adaptive Volterra filters, several of their practical implementations consider some type of sparseness for complexity reduction. Such implementations are obtained using application-oriented strategies to prune a standard Volterra filter by zeroing some of its coefficients. In this context, the main challenge is to choose a pruning strategy that leads to minimum loss of performance. Meeting this challenge is not a trivial task because of the variety of strategies available for obtaining pruned Volterra filters as well as due to the lack of a theoretical framework describing these strategies in a general scenario. Thus, the primary objective of this research work is to establish a basis for assessing adaptive pruned Volterra filters. For such, a unifying scheme describing the input vectors of different pruned Volterra implementations is proposed along with an extended version of a constrained approach used to represent sparseness in adaptive filters. Based on this foundation, an analysis of the performance of adaptive pruned Volterra filters in terms of the minimum mean-square error is carried out. Simulation results are presented attesting the effectiveness of the proposed approach.
► A new foundation for the analysis of adaptive pruned Volterra filters is proposed. ► The different pruned Volterra filters are described using a novel unifying scheme. ► An extension of a constrained approach for analyzing adaptive filters is introduced. ► A minimum mean-square-error analysis is performed based on the proposed foundation. ► Results of numerical simulation attest the effectiveness of the presented theory.