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      Robust adaptive filtering algorithms for α-stable random processes

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      Author
      Aydin, G.
      Arıkan, Orhan
      Çetin, A. Enis
      Date
      1999-02
      Source Title
      IEEE Transactions on Circuits and Systems II : Analog and Digital Signal Processing
      Print ISSN
      1057-7130
      Publisher
      Institute of Electrical and Electronics Engineers
      Volume
      46
      Issue
      2
      Pages
      198 - 202
      Language
      English
      Type
      Article
      Item Usage Stats
      309
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      273
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      Abstract
      A new class of algorithms based on the fractional lower order statistics is proposed for finite-impulse response adaptive filtering in the presence of α-stable processes. It is shown that the normalized least mean p-norm (NLMP) and Douglas' family of normalized least mean square algorithms are special cases of the proposed class of algorithms. A convergence proof for the new algorithm is given by showing that it performs a descent-type update of the NLMP cost function. Simulation studies indicate that the proposed algorithms provide superior performance in impulsive noise environments compared to the existing approaches.
      Keywords
      Adaptive filtering
      FIR filters
      Mathematical models
      Probability density function
      Random processes
      Spurious signal noise
      Stability
      Statistical methods
      Alpha stable random processes
      Finite impulse response adaptive filtering
      Impulsive signals
      Least mean square algorithms
      Normalized least mean p norm
      Adaptive algorithms
      Permalink
      http://hdl.handle.net/11693/25271
      Published Version (Please cite this version)
      http://dx.doi.org/10.1109/82.752953
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      • Department of Electrical and Electronics Engineering 3624
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