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

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Abstract

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.

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Notes

  1. With QPSK input, the adaptation of a(n) of NSA-NSA is given as \(a(n+1)\hbox { }=a(n)+\rho _a \mathrm{conj}\{\mathrm{sign}\{e(n)\}\}[y_1 (n)-y_2 (n)]\lambda (n)[1-\lambda (n)]\), where \(\mathrm{conj}\{\cdot \}\) denotes conjugate operation.

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    Impulsive noise in ISI channel

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  2. The derivation of VSS-NSA, VSS-APSA, and NRMN is different from the original literatures, when input signal is the complex number. For paper length optimization, and in order to focus on the simplicity of the proposed approach, we have decided to only compare to NLMS-NSA algorithm.

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Acknowledgments

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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Authors and Affiliations

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

    Lu Lu & Haiquan Zhao

  2. Computational Neuro-Engineering Laboratory, University of Florida, Gainesville, FL, 32611, USA

    Kan Li

  3. School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, China

    Badong Chen

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  1. Lu Lu
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Correspondence to Haiquan Zhao.

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Lu, L., Zhao, H., Li, K. et al. A Novel Normalized Sign Algorithm for System Identification Under Impulsive Noise Interference. Circuits Syst Signal Process 35, 3244–3265 (2016). https://doi.org/10.1007/s00034-015-0195-1

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