On the performance of adaptive pruned Volterra filters

Authors:
Eduardo Luiz Ortiz Batista;Rui Seara
Affiliations:
Department of Informatics and Statistics, Federal University of Santa Catarina, 88040-900 Florianópolis, SC, Brazil;LINSE: Circuits and Signal Processing Laboratory, Department of Electrical Engineering, Federal University of Santa Catarina, 88040-900 Florianópolis, SC, Brazil
Venue:
Signal Processing
Year:
2013

Citing 9
Cited 0

Adaptive Filters: Theory and Applications

Adaptive Filters: Theory and Applications
Convex Optimization

Convex Optimization
Orthogonalized power filters for nonlinear acoustic echo cancellation

Signal Processing
Adaptive reduced-rank processing based on joint and iterative interpolation, decimation, and filtering

IEEE Transactions on Signal Processing
A sparse-interpolated scheme for implementing adaptive volterra filters

IEEE Transactions on Signal Processing
Baseband Volterra filters for implementing carrier basednonlinearities

IEEE Transactions on Signal Processing
A multichannel hierarchical approach to adaptive Volterra filters employing filtered-X affine projection algorithms

IEEE Transactions on Signal Processing
Adaptive Volterra Filters With Evolutionary Quadratic Kernels Using a Combination Scheme for Memory Control

IEEE Transactions on Signal Processing
A fully LMS/NLMS adaptive scheme applied to sparse-interpolated Volterra filters with removed boundary effect

Signal Processing

Quantified Score

Hi-index	0.08

Visualization

Abstract

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.