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Gradient optimization p-norm-like constraint LMS algorithm for sparse system estimation
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DOI：10.1016/j.sigpro.2012.10.008.png)
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Abstract
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In order to improve the sparsity exploitation performance of norm constraint least mean square (LMS) algorithms, a novel adaptive algorithm is proposed by introducing a variable p-norm-like constraint into the cost function of the LMS algorithm, which exerts a zero attraction to the weight updating iterations. The parameter p of the p-norm-like constraint is adjusted iteratively along the negative gradient direction of the cost function. Numerical simulations show that the proposed algorithm has better performance than traditional l(0) and l(1) norm constraint LMS algorithms. (c) 2012 Elsevier B.V. All rights reserved.
Keywords:
p-norm-like constraint
Norm constraint
Least mean square algorithm
Sparsity
Gradient optimization
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