Online Tracking of the Degree of Nonlinearity Within Complex Signals

Abstract

A novel method for online tracking of the changes in the nonlinearity within complex-valued signals is introduced. This is achieved by a collaborative adaptive signal processing approach by means of a hybrid filter. By tracking the dynamics of the adaptive mixing parameter within the employed hybrid filtering architecture, we show that it is possible to quantify the degree of nonlinearity within complex-valued data. Simulations on both benchmark and real world data support the approach.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 2008
Accession Number
ADA505362

Entities

People

  • Beth Jelfs
  • Danilo P Mandic
  • Kazuyuki Aihara
  • Phebe Vayanos
  • Soroush Javidi

Organizations

  • Imperial College London

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Abstracts
  • Adaptive Filters
  • Algorithms
  • Artificial Intelligence Computing
  • Complex Numbers
  • Data Sets
  • Dimensionality Reduction
  • Filters
  • Gaussian Noise
  • Iterations
  • Learning
  • Machine Learning
  • Neural Networks
  • Numbers
  • Signal Processing
  • Simulations

Fields of Study

  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Image Processing and Computer Vision.