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Shrinkage tuning based on an unbiased MSE estimate for sparsity-aware adaptive filtering | IEEE Conference Publication | IEEE Xplore

Shrinkage tuning based on an unbiased MSE estimate for sparsity-aware adaptive filtering


Abstract:

Effective utilization of sparsity of the system to be estimated is a key to achieve excellent adaptive filtering performances. This can be realized by the adaptive proxim...Show More

Abstract:

Effective utilization of sparsity of the system to be estimated is a key to achieve excellent adaptive filtering performances. This can be realized by the adaptive proximal forward-backward splitting (APFBS) with carefully chosen parameters. In this paper, we propose a systematic parameter tuning based on a minimization principle of an unbiased MSE estimate. Thanks to the piecewise quadratic structure of the proposed MSE estimate, we can obtain its minimizer with low computational load. A numerical example demonstrates the efficacy of the proposed parameter tuning by its excellent performance over a broader range of SNR than a heuristic parameter tuning of the APFBS.
Date of Conference: 04-09 May 2014
Date Added to IEEE Xplore: 14 July 2014
Electronic ISBN:978-1-4799-2893-4

ISSN Information:

Conference Location: Florence, Italy

References

References is not available for this document.