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Low-Complexity Non-Uniform Penalized Affine Projection Algorithm for Sparse System Identification

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Abstract

In this paper, an improved sparse-aware affine projection (AP) algorithm for sparse system identification is proposed and investigated. The proposed sparse AP algorithm is realized by integrating a non-uniform norm constraint into the cost function of the conventional AP algorithm, which can provide a zero attracting on the filter coefficients according to the value of each filter coefficient. Low complexity is obtained by using a linear function instead of the reweighting term in the modified AP algorithm to further improve the performance of the proposed sparse AP algorithm. The simulation results demonstrate that the proposed sparse AP algorithm outperforms the conventional AP and previously reported sparse-aware AP algorithms in terms of both convergence speed and steady-state error when the system is sparse.

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Acknowledgments

This work was partially supported by Pre-Research Fund of the 12th Five-Year Plan (no. 4010403020102). This paper is also supported by Fundamental Research Funds for the Central Universities (HEUCFD1433).

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Authors and Affiliations

  1. College of Information and Communications Engineering, Harbin Engineering University, Harbin, 150001, China

    Yingsong Li & Chaozhu Zhang

  2. School of Mechanical and Electric Engineering, Heilongjiang University, Harbin, 150080, China

    Shigang Wang

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  1. Yingsong Li
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  2. Chaozhu Zhang
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  3. Shigang Wang
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Correspondence to Yingsong Li.

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Li, Y., Zhang, C. & Wang, S. Low-Complexity Non-Uniform Penalized Affine Projection Algorithm for Sparse System Identification. Circuits Syst Signal Process 35, 1611–1624 (2016). https://doi.org/10.1007/s00034-015-0132-3

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