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\(L_{1}\)-norm constrained normalized subband adaptive filter algorithm with variable norm-bound parameter and improved version

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Abstract

The \(L_{1}\)-norm constrained normalized subband adaptive filter with variable norm-bound parameter \((L_{1}\hbox {NCNSAF-V})\) algorithm and its variable step size version VSS-\(L_{1}\)NCNSAF-V are proposed in this paper, which are more superior to some existing algorithms in the sparse system. The proposed \(L_{1}\)NCNSAF-V is derived by using the Lagrange multiplier method, and the VSS-\(L_{1}\)NCNSAF-V is obtained by minimizing the statistical square of the Euclidean norm of the noise-free subband a posterior error vector. The simulation results demonstrate that the proposed algorithms achieve good performance.

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Acknowledgements

This work was partially supported by National Science Foundation of P.R. China (Grant Nos. 61571374, 61271340, 61433011).

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Authors and Affiliations

  1. Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle, Ministry of Education, and the School of Electrical Engineering, Southwest Jiaotong University, Chengdu, 610031, China

    Long Shi & Haiquan Zhao

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  1. Long Shi
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  2. Haiquan Zhao
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Correspondence to Haiquan Zhao.

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Shi, L., Zhao, H. \(L_{1}\)-norm constrained normalized subband adaptive filter algorithm with variable norm-bound parameter and improved version. SIViP 11, 865–871 (2017). https://doi.org/10.1007/s11760-016-1033-z

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  • DOI: https://doi.org/10.1007/s11760-016-1033-z

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