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Partially Decoupled Volterra Filters: Formulation and LMS Adaptation (1994)
| Venue: | IEEE Transactions on Signal Processing |
| Citations: | 7 - 2 self |
BibTeX
@TECHREPORT{Griffith94partiallydecoupled,
author = {David W. Griffith and Jr. and Gonzalo R. Arce},
title = {Partially Decoupled Volterra Filters: Formulation and LMS Adaptation},
institution = {IEEE Transactions on Signal Processing},
year = {1994}
}
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Abstract
The adaptation of Volterra filters by one particular method, the method of least mean squares (LMS), while easily implemented, is complicated by the fact that upper bounds for the values of step sizes employed by a parallel update LMS scheme are difficult to obtain. In this paper, we propose a modification of the Volterra filter in which the filter weights of a given order are optimized independently of those weights of higher order. Using this approach, we then solve the MMSE filtering problem as a series of constrained optimization problems, which produce a partially decoupled normal equation for the Volterra filter. From this normal equation, we are able to develop an adaptation routine which uses the principles of partial decoupling which is similar in form to the Volterra LMS algorithm, but with important structural differences that allow a straightforward derivation of bounds on the algorithm's step sizes; these bounds can be shown to depend on the respective diagonal blocks of t...
Keyphrases
decoupled volterra filter volterra filter lm adaptation normal equation straightforward derivation important structural difference step size mmse filtering problem partial decoupling constrained optimization problem parallel update lm scheme volterra lm algorithm mean square particular method filter weight upper bound respective diagonal block adaptation routine
