Circuits, Systems, and Signal Processing

, Volume 36, Issue 3, pp 1322–1339 | Cite as

Nonparametric Variable Step-Size LMAT Algorithm

  • Sihai Guan
  • Zhi LiEmail author
Short Paper


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.


LMAT Variable step-size Impulsive noise Nonparametric Most of the noise densities System identification 



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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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.School of Electro-Mechanical Engineering Xidian UniversityXi’anChina

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