Maximum correntropy criterion based sparse adaptive filtering algorithms for robust channel estimation under non-Gaussian environments

DOI10.1016/j.jfranklin.2015.03.039zbMath1395.93544arXiv1503.00802OpenAlexW1993405482MaRDI QIDQ1660502

Hua Qu, Guan Gui, Wentao Ma, Li Xu, Badong Chen, Ji-Hong Zhao

Publication date: 16 August 2018

Published in: Journal of the Franklin Institute (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1503.00802

zbMATH Keywords

broadband wireless communicationMaximum correntropy criterionrobust channel estimationsparse adaptive filtering algorithms

Mathematics Subject Classification ID

Filtering in stochastic control theory (93E11) Sensitivity (robustness) (93B35) Estimation and detection in stochastic control theory (93E10) Adaptive or robust stabilization (93D21) Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

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Cites Work

• A family of proportionate normalized subband adaptive filter algorithms
• Adaptive algorithms for sparse echo cancellation
• Detection of fading overlapping multipath components
• Convergence analysis of sparse LMS algorithms with \(l_{1}\)-norm penalty based on white input signal
• Sparse leaky-LMS algorithm for system identification and its convergence analysis
• Correntropy: Properties and Applications in Non-Gaussian Signal Processing
• SPARLS: The Sparse RLS Algorithm
• Non-Gaussian noise models in signal processing for telecommunications: new methods an results for class A and class B noise models
• Nonlinear system identification in impulsive environments
• Robust Principal Component Analysis Based on Maximum Correntropy Criterion

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