Shrinkage Variable Regularization Matrix NSAF
Shrinkage Variable Regularization Matrix NSAF
- Author(s): Wei Hu and Jingen Ni
- DOI: 10.1049/cp.2015.0928
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- Author(s): Wei Hu and Jingen Ni Source: 6th International Conference on Wireless, Mobile and Multi-Media (ICWMMN 2015), 2015 page ()
- Conference: 6th International Conference on Wireless, Mobile and Multi-Media (ICWMMN 2015)
- DOI: 10.1049/cp.2015.0928
- ISBN: 978-1-78561-046-2
- Location: Beijing, China
- Conference date: 20-23 Nov. 2015
- Format: PDF
The normalized subband adaptive filter (NSAF) has a faster convergence rate than the normalized least mean square (NLMS) algorithm for correlated inputs, and its computational complexity is close to that of the NLMS. However, the NSAF suffers from a tradeoff between fast convergence rate and low steady-state misalignment. To address this problem, in this paper we propose a shrinkage variable regularization matrix NSAF (SVRM-NSAF). Its computational complexity almost does not increase compared to the NSAF. The proposed algorithm is derived by minimizing the powers of the noise-free a posterior subband errors. In order to estimate the required noise-free a posterior subband errors, an l1-l2 minimization method is used. Simulation results show that the proposed algorithm can obtain both fast convergence rate and low steady-state misalignment.
Inspec keywords: matrix algebra; computational complexity; minimisation; adaptive filters
Subjects: Algebra; Signal processing theory; Optimisation techniques; Algebra; Filtering methods in signal processing; Optimisation techniques; Computational complexity
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