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Collaborative Adaptive Filters for Online Knowledge Extraction and Information Fusion

  • Beth Jelfs
  • Phebe Vayanos
  • Soroush Javidi
  • Vanessa Su Lee Goh
  • Danilo Mandic

We present a method for extracting information (or knowledge) about the nature of a signal. This is achieved by employing recent developments in signal characterisation for online analysis of the changes in signal modality. We show that it is possible to use the fusion of the outputs of adaptive filters to produce a single collaborative hybrid filter and that by tracking the dynamics of the mixing parameter of this filter rather than the actual filter performance, a clear indication as to the nature of the signal is given. Implementations of the proposed hybrid filter in both the real R and the complex C domains are analysed and the potential of such a scheme for tracking signal nonlinearity in both domains is highlighted. Simulations on linear and nonlinear signals in a prediction configuration support the analysis; real world applications of the approach have been illustrated on electroencephalogram (EEG), radar and wind data.

Keywords

Input Signal Convex Combination Radar Data Tracking Capability Normalise Little Mean Square 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Beth Jelfs
    • 1
  • Phebe Vayanos
    • 1
  • Soroush Javidi
    • 1
  • Vanessa Su Lee Goh
    • 2
  • Danilo Mandic
    • 1
  1. 1.Imperial College LondonLondonUK
  2. 2.Nederlandse Aardolie MaatschappijNetherlands

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