Lawson-norm Constrained Generalized Lncosh Based Adaptive Algorithm for Sparse System Identification

LI Yingsong; LIANG Tao; ZHANG Xiangkun; JIANG Jingshan

doi:10.11999/JEIT210057

Volume 44 Issue 2

Feb. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(2): 654-660

LI Yingsong, LIANG Tao, ZHANG Xiangkun, JIANG Jingshan. Lawson-norm Constrained Generalized Lncosh Based Adaptive Algorithm for Sparse System Identification[J]. Journal of Electronics & Information Technology, 2022, 44(2): 654-660. doi: 10.11999/JEIT210057

Citation:

LI Yingsong, LIANG Tao, ZHANG Xiangkun, JIANG Jingshan. Lawson-norm Constrained Generalized Lncosh Based Adaptive Algorithm for Sparse System Identification[J]. Journal of Electronics & Information Technology, 2022, 44(2): 654-660. doi: 10.11999/JEIT210057

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Lawson-norm Constrained Generalized Lncosh Based Adaptive Algorithm for Sparse System Identification

doi: 10.11999/JEIT210057

1.
College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
2.
National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China

Received Date: 2021-01-18
Rev Recd Date: 2021-07-15

Available Online: 2021-07-19

Publish Date: 2022-02-25

Abstract

Abstract

A generalized Lawson-lncosh adaptive filtering algorithm for sparse system identification is proposed. The proposed algorithm is derived by constructing a new cost function consisted of Lawson-norm of system coefficient vector and lncosh function of instantaneous error. And the Lawson-norm constraint introduces a parameter p which can realize the dynamic adjustment of sparsity. The proposed algorithm can improve the convergence speed and reduce the steady-state error for sqarse system identification, where the Lncosh function of the error has the property of combating impulsive noise. Then, the range of the step-size and effect of parameter p on the proposed algorithm are analyzed. Computer simulation results show that the proposed algorithm has superior performance compared with other existing algorithms with Gaussian and colored input signals and the sparsity constraint for the proposed algorithm is controllable.
- Sparse system identification,
- Adaptive filtering,
- Lawson norm,
- Impulsive noise

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References(17)

References

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