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Circuits, Systems, and Signal Processing

, Volume 35, Issue 5, pp 1611–1624 | Cite as

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

  • Yingsong LiEmail author
  • Chaozhu Zhang
  • Shigang Wang
Article

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.

Keywords

Affine projection algorithm Sparse system identification  Zero attracting Norm penalty Adaptive filtering 

Notes

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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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.College of Information and Communications EngineeringHarbin Engineering UniversityHarbinChina
  2. 2.School of Mechanical and Electric EngineeringHeilongjiang UniversityHarbinChina

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