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Sparsity-Aware Adaptive Algorithms Based on Alternating Optimization and Shrinkage
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DOI：10.1109/LSP.2014.2298116.png)
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This letter proposes a novel sparsity-aware adaptive filtering scheme and algorithms based on an alternating optimization strategy with shrinkage. The proposed scheme employs a two-stage structure that consists of an alternating optimization of a diagonally-structured matrix that speeds up the convergence and an adaptive filter with a shrinkage function that forces the coefficients with small magnitudes to zero. We devise alternating optimization least-mean square (LMS) algorithms for the proposed scheme and analyze its mean-square error. Simulations for a system identification application show that the proposed scheme and algorithms outperform in convergence and tracking existing sparsity-aware algorithms.
Keyword:
Adaptive filters
iterative methods
sparse signal processing
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IF:
9.6
论文数:
1.1W
被引数:
1.7W
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