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Robust Sparse Normalized LMAT Algorithms for Adaptive System Identification Under Impulsive Noise Environments

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Abstract

It is known that the conventional adaptive filtering algorithms can have good performance for non-sparse systems identification, but unsatisfactory performance for sparse systems identification. The normalized least mean absolute third (NLMAT) algorithm which is based on the high-order error power criterion has a strong anti-jamming capability against the impulsive noise, but reduced estimation performance in case of sparse systems. In this paper, several sparse NLMAT algorithms are proposed by inducing sparse-penalty functions into the standard NLMAT algorithm in order to exploit the system sparsity. Simulation results are given to validate that the proposed sparse algorithms can achieve a substantial performance improvement for a sparse system and robustness to impulsive noise environments.

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Acknowledgements

The work of Felix Albu was supported by a grant from the Romanian National Authority for Scientific research and Innovation, CNCS/CCCDI-UEFISCDI project number PN-III-P4-ID-PCE-2016-0339.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, National Institute of Technology Warangal, Warangal, 506004, India

    Rakesh Pogula & T. Kishore Kumar

  2. Department of Electronics, Valahia University of Targoviste, 130082, Targoviste, Romania

    Felix Albu

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Pogula, R., Kumar, T.K. & Albu, F. Robust Sparse Normalized LMAT Algorithms for Adaptive System Identification Under Impulsive Noise Environments. Circuits Syst Signal Process 38, 5103–5134 (2019). https://doi.org/10.1007/s00034-019-01111-3

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