Chapter 4 - Recent advances on LIP nonlinear filters and their applications: Efficient solutions and significance-aware filtering

Article in Edited Volumes
(Book chapter)


Publication Details

Author(s): Hofmann C, Kellermann W
Title edited volumes: Adaptive learning methods for nonlinear system modeling
Publisher: Butterworth-Heinemann
Publication year: 2018
Pages range: 71-102
ISBN: 978-0-12-812976-0
Language: English


Abstract

Linear-In-the-Parameters (LIP) nonlinear filters are categorized as
Cascade Models (CMs) (generalizing Hammerstein models), Cascade Group
Models (CGMs) (generalizing Hammerstein Group models (HGMs) and
including, e.g., Volterra filters) and bilinear cascade models,
where the filter output is a bilinear function of the model parameters.
Time-domain and partitioned-block frequency-domain adaptation of CGMs
and CMs is described and the methods for adapting bilinear cascade
models are summarized as variants of the filtered-X adaptation. These
models and algorithms are employed to review the Significance-Aware (SA)
filtering concept, decomposing the model for the unknown system and the
adaptation mechanism into synergetic subsystems to achieve high
computational efficiency. In particular, the Serial SA (SSA) and
Parallel SA (PSA) decomposition lead to SSA-CMs, PSA-CGMs and a novel
PSA filtered-X algorithm. The main concepts described in this chapter
are exemplarily compared for the challenging application of nonlinear
acoustic echo cancellation. Furthermore, model structure estimation for
LIP nonlinear filters based on convex filter combinations is briefly
outlined and compared with SA filtering.


FAU Authors / FAU Editors

Kellermann, Walter Prof. Dr.-Ing.
Professur für Nachrichtentechnik


How to cite

APA:
Hofmann, C., & Kellermann, W. (2018). Chapter 4 - Recent advances on LIP nonlinear filters and their applications: Efficient solutions and significance-aware filtering. In Adaptive learning methods for nonlinear system modeling. (pp. 71-102). Butterworth-Heinemann.

MLA:
Hofmann, Christian, and Walter Kellermann. "Chapter 4 - Recent advances on LIP nonlinear filters and their applications: Efficient solutions and significance-aware filtering." Adaptive learning methods for nonlinear system modeling. Butterworth-Heinemann, 2018. 71-102.

BibTeX: 

Last updated on 2019-17-01 at 11:15