Detecting Instability Potentials in Regularization for Fast Affine Projection Algorithms

In fast affine projection (FAP) adaptation algorithms, it is needed to explicitly or implicitly perform a matrix inversion, during which a small positive regularization factor plays an important role in keeping the algorithm stable and optimized. While existing schemes choose the regularization fact...

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Bibliographic Details
Published in2007 Conference Record of the Forty-First Asilomar Conference on Signals, Systems and Computers pp. 1623 - 1627
Main Author Heping Ding
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2007
Subjects
Adaptive equalizers
Adaptive filters
Convergence
Councils
Financial advantage program
Noise cancellation
Projection algorithms
Size control
Stability
Steady-state
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Summary:In fast affine projection (FAP) adaptation algorithms, it is needed to explicitly or implicitly perform a matrix inversion, during which a small positive regularization factor plays an important role in keeping the algorithm stable and optimized. While existing schemes choose the regularization factor based on certain system criteria not related to the inversion, this paper proposes a simple scheme that dynamically diagnoses the inversion process itself for potentials of instability. This work paves the way for further studies on "minimal regularization and step-size control" technique. A FAP adopting this technique can be compared with FAPs with existing regularization schemes for convergence and steady state performance.
ISBN:9781424421091
1424421098
ISSN:1058-6393
2576-2303
DOI:10.1109/ACSSC.2007.4487506