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Robust variable-regularized RLS algorithms | IEEE Conference Publication | IEEE Xplore

Abstract:

The regularization parameter is required in most (if not all) adaptive algorithms, while its role becomes very critical in the presence of additive noise. In this paper, ...Show More

Abstract:

The regularization parameter is required in most (if not all) adaptive algorithms, while its role becomes very critical in the presence of additive noise. In this paper, we focus on the regularized recursive least-squares (RLS) algorithm and present a method to find its regularization parameter, which is related to the signal-to-noise ratio (SNR). Also, using a proper estimation of the SNR, we further propose a variable-regularized RLS (VR-RLS) algorithm. In addition, a low-complexity version of the VR-RLS algorithm is developed, based on the dichotomous coordinate descent (DCD) method. Due to their nature, the proposed algorithms have good robustness features against additive noise, which make them behave well in all noisy conditions. Simulations performed in the context of acoustic echo cancellation support these findings.
Date of Conference: 01-03 March 2017
Date Added to IEEE Xplore: 13 April 2017
ISBN Information:
Conference Location: San Francisco, CA, USA

References

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