Circuits, Systems, and Signal Processing

, Volume 35, Issue 9, pp 3244–3265 | Cite as

A Novel Normalized Sign Algorithm for System Identification Under Impulsive Noise Interference

  • Lu Lu
  • Haiquan Zhao
  • Kan Li
  • Badong Chen


To overcome the performance degradation of adaptive filtering algorithms in the presence of impulsive noise, a novel normalized sign algorithm (NSA) based on a convex combination strategy, called NSA-NSA, is proposed in this paper. The proposed algorithm is capable of solving the conflicting requirement of fast convergence rate and low steady-state error for an individual NSA filter. To further improve the robustness to impulsive noises, a mixing parameter updating formula based on a sign cost function is derived. Moreover, a tracking weight transfer scheme of coefficients from a fast NSA filter to a slow NSA filter is proposed to speed up the convergence rate. The convergence behavior and performance of the new algorithm are verified by theoretical analysis and simulation studies.


Adaptive filtering Convex combination Normalized sign algorithm System identification Impulsive noise 



The authors want to express their deep thanks to the anonymous reviewers for many valuable comments which greatly helped to improve the quality of this work. This work was supported in part by National Natural Science Foundation of China (Grants: 61271340, 61571374, 61134002, 61433011, U1234203), the Sichuan Provincial Youth Science and Technology Fund (Grant: 2012JQ0046), and the Fundamental Research Funds for the Central Universities (Grant: SWJTU12CX026).


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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle, Ministry of Education, and School of Electrical EngineeringSouthwest Jiaotong UniversityChengduChina
  2. 2.Computational Neuro-Engineering LaboratoryUniversity of FloridaGainesvilleUSA
  3. 3.School of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’anChina

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