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An optimized NLMS algorithm for system identification
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DOI：10.1016/j.sigpro.2015.06.016.png)
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Abstract
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The normalized least-mean-square (NLMS) adaptive filter is widely used in system identification. In this paper, we develop an optimized NLMS algorithm, in the context of a state variable model. The proposed algorithm follows a joint-optimization problem on both the normalized step-size and regularization parameters, in order to minimize the system misalignment. Consequently, it achieves a proper compromise between the performance criteria, i.e., fast convergence/tracking and low misadjustment. Simulations performed in the context of acoustic echo cancellation indicate the good features of the proposed algorithm. (C) 2015 Elsevier B.V. All rights reserved.
Keywords:
Echo cancellation
Normalized least-mean-square (NLMS) algorithm
Variable step-size NLMS
Variable regularized NLMS
Adaptive filters
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