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

  • Rakesh PogulaEmail author
  • T. Kishore Kumar
  • Felix Albu
Article
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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.

Keywords

Sparse system identification Adaptive filtering Normalized least mean absolute third (NLMAT) algorithm High-order error power Impulsive noise 

Notes

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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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringNational Institute of Technology WarangalWarangalIndia
  2. 2.Department of ElectronicsValahia University of TargovisteTargovisteRomania

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