Diffusion normalized Huber adaptive filtering algorithm

DOI10.1016/j.jfranklin.2018.03.001zbMath1390.93796OpenAlexW2789663101WikidataQ130135217 ScholiaQ130135217MaRDI QIDQ1643241

Zhi Li, Sihai Guan

Publication date: 19 June 2018

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

Full work available at URL: https://doi.org/10.1016/j.jfranklin.2018.03.001

zbMATH Keywords

computational complexitydistributed estimationadaptive filtering algorithmimpulsive interference environmentsmean stability performance

Mathematics Subject Classification ID

Filtering in stochastic control theory (93E11) Adaptive control/observation systems (93C40) Estimation and detection in stochastic control theory (93E10) Agent technology and artificial intelligence (68T42)

Related Items (7)

Robust multitask diffusion affine projection M-estimate algorithm: design and performance analysis ⋮ Nonlinear Consensus+Innovations under Correlated Heavy-Tailed Noises: Mean Square Convergence Rate and Asymptotics ⋮ Distributed Recursive Estimation under Heavy-Tail Communication Noise ⋮ Robust diffusion Huber-based normalized least mean square algorithm with adjustable thresholds ⋮ Robust least mean logarithmic square adaptive filtering algorithms ⋮ Variational Bayesian adaptation of process noise covariance matrix in Kalman filtering ⋮ Diffusion-probabilistic least mean square algorithm

Cites Work

• Nonparametric variable step-size LMAT algorithm
• Diffusion recursive least-squares for distributed estimation over adaptive networks
• Diffusion Least-Mean Squares Over Adaptive Networks: Formulation and Performance Analysis
• Diffusion LMS Strategies for Distributed Estimation
• Diffusion Sparse Least-Mean Squares Over Networks
• Robust Statistics

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