Abstract
In equalization and deconvolution tasks, the correlated nature of the input signal slows the convergence speeds of stochastic gradient adaptive filters. Prewhitening techniques have been proposed to improve the convergence performance, but the additional coefficient memory and updates for the prewhitening filter can be prohibitive in some applications. We present two simple algorithms that employ the equalizer as a prewhitening filter within the gradient updates. These self-whitening algorithms provide quasi-Newton convergence locally about the optimum coefficient solution for deconvolution and equalization tasks. Multichannel extensions of the techniques are also described.
Keywords
DeconvolutionEqualization (audio)AlgorithmConvergence (economics)Blind deconvolutionAdaptive filterAdaptive equalizerFilter (signal processing)Computer scienceMatched filterFinite impulse responseBlind equalizationIntersymbol interferenceMathematicsComputer vision
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Publication Info
- Year
- 1999
- Type
- article
- Volume
- 47
- Issue
- 4
- Pages
- 1161-1165
- Citations
- 35
- Access
- Closed
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Cite This
S.C. Douglas,
Andrzej Cichocki,
S. Amari
(1999).
Self-whitening algorithms for adaptive equalization and deconvolution.
IEEE Transactions on Signal Processing
, 47
(4)
, 1161-1165.
https://doi.org/10.1109/78.752617
Identifiers
- DOI
- 10.1109/78.752617