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Nonparametric Variable Step-Size LMAT Algorithm

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Abstract

This paper proposes a nonparametric variable step-size least mean absolute third (NVSLMAT) algorithm to improve the capability of the adaptive filtering algorithm against the impulsive noise and other types of noise. The step-size of the NVSLMAT is obtained using the instantaneous value of a current error estimate and a posterior error estimate. This approach is different from the traditional method of nonparametric variance estimate. In the NVSLMAT algorithm, fewer parameters need to be set, thereby reducing the complexity considerably. Additionally, the mean of the additive noise does not necessarily equal zero in the proposed algorithm. In addition, the mean convergence and steady-state mean-square deviation of the NVSLMAT algorithm are derived and the computational complexity of NVSLMAT is analyzed theoretically. Furthermore, the experimental results in system identification applications presented illustrate the principle and efficiency of the NVSLMAT algorithm.

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Acknowledgments

This work was partially supported by the National Natural Science Foundation of China (Grant: 61074120) and the Ph.D. Programs Foundation of the Ministry of Education of China (Grant: 20110203110004).

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Authors and Affiliations

  1. School of Electro-Mechanical Engineering Xidian University, 2 South Taibai Road, Xi’an, 710071, Shaanxi, China

    Sihai Guan & Zhi Li

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  1. Sihai Guan
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  2. Zhi Li
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Correspondence to Zhi Li.

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Guan, S., Li, Z. Nonparametric Variable Step-Size LMAT Algorithm. Circuits Syst Signal Process 36, 1322–1339 (2017). https://doi.org/10.1007/s00034-016-0356-x

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  • DOI: https://doi.org/10.1007/s00034-016-0356-x

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