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Sparse Mixed Norm Adaptive Filtering Technique

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Abstract

In this paper, we would suggest a sparse adaptive filtering technique which is robust against Gaussian and non-Gaussian noises. To this goal, a linear combination of the least mean square and the least mean fourth loss functions has been considered as the fidelity term. Moreover, in order to promote the sparsity property of the underlying vector, we have added different sparsity-inducing penalty terms. To optimize the resultant cost function, the quasi-Newton scheme has been adopted which accelerates the convergence of the algorithm. The convergence of the proposed method has been proved analytically. Furthermore, the efficiency of the suggested scheme has been evaluated through extensive simulation scenarios which confirm the superiority of the proposed algorithm over the other state-of-the-art schemes.

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Authors and Affiliations

  1. Laboratory of Wireless Communications and Signal Processing (WCSP), Faculty of Electrical Engineering, Sahand University of Technology, Tabriz, Iran

    Nafiseh Maleki & Masoumeh Azghani

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  1. Nafiseh Maleki
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  2. Masoumeh Azghani
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Correspondence to Masoumeh Azghani.

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Maleki, N., Azghani, M. Sparse Mixed Norm Adaptive Filtering Technique. Circuits Syst Signal Process 39, 5758–5775 (2020). https://doi.org/10.1007/s00034-020-01432-8

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