Complex-Valued Nonlinear Adaptive Filters with Applications in α-Stable Environments

Sayed Pouria Talebi*, Stefan Werner, Danilo P. Mandic

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

6 Citations (Scopus)
125 Downloads (Pure)

Abstract

A nonlinear adaptive filtering framework for processing complex-valued signals is derived. The introduced adaptive filter extends the fractional-order framework of the authors for dealing with real-valued signals to the complex domain via the augmented statistical approach to complex-valued signal processing. This results in a versatile class of adaptive filtering techniques, which allows the classical Gaussian assumption to be extended to the generalized context of α-stables. For rigor, the performance of the introduced adaptive filtering framework is analyzed, its convergence criteria is established, and its application in tracking signals of chaotic systems is demonstrated using simulations.

Original languageEnglish
Article number8768059
Pages (from-to)1315-1319
Number of pages5
JournalIEEE Signal Processing Letters
Volume26
Issue number9
DOIs
Publication statusPublished - 1 Sep 2019
MoE publication typeA1 Journal article-refereed

Keywords

  • complex-valued a-stable signals
  • fractional-order adaptive filtering
  • Nonlinear adaptive filtering

Fingerprint

Dive into the research topics of 'Complex-Valued Nonlinear Adaptive Filters with Applications in α-Stable Environments'. Together they form a unique fingerprint.

Cite this