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Logarithmic reweighting nonnegative least mean square algorithm

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Abstract

System identification based on least mean square (LMS) adaptive filters is effective due to their simplicity and robustness. Inherent physical characteristic of intended system usually make nonnegativity constraint desirable. In other words, imposing nonnegativity constraint on optimization problem leads to more feasible unknown parameter estimation. Hence, nonnegative least mean square (NNLMS) and its variants were proposed to adaptively solve the Wiener filtering problem considering constraint that makes filter weights nonnegative. In this paper, we propose a new variant of nonnegative least mean square for which its performance is analyzed both theoretically and experimentally. The proposed algorithm behavior is investigated in sparse system identification by Monte Carlo simulations in order to show validation of analysis and theory models. We compare our method with IP-NNLMS and NNLMS in order to prove the advantage of our proposed algorithm. Our proposed algorithm is also used in classification problem, and it is compared with entropy function-based online adaptive decision fusion (EADF) algorithm.

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Authors and Affiliations

  1. Isfahan University of Technology, Electrical and Computer Engineering Isfahan, Isfahan Blvd Isfahan, Isfahan, Iran

    Seyed Moslem Shokrolahi

  2. Shahid Sattari Aeronautical University of Science and Technology, Electrical Engineering Tehran, Tehran, Iran

    Mansour Nejati Jahromi

Authors
  1. Seyed Moslem Shokrolahi
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  2. Mansour Nejati Jahromi
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Correspondence to Seyed Moslem Shokrolahi.

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Shokrolahi, S.M., Jahromi, M.N. Logarithmic reweighting nonnegative least mean square algorithm. SIViP 12, 51–57 (2018). https://doi.org/10.1007/s11760-017-1129-0

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  • DOI: https://doi.org/10.1007/s11760-017-1129-0

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