Diffusion Normalized Maximum Versoria Criterion Robust to Impulsive Noise

Zandi, S; Korki, M

Diffusion Normalized Maximum Versoria Criterion Robust to Impulsive Noise

Zandi, S

Korki, M

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Circuits and Systems II: Express Briefs, 2023, 70, (4), pp. 1660-1664
Issue Date:: 2023-04-01

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Field	Value	Language
dc.contributor.author	Zandi, S https://orcid.org/0000-0003-2247-5595
dc.contributor.author	Korki, M
dc.date.accessioned	2024-07-05T01:25:41Z
dc.date.available	2024-07-05T01:25:41Z
dc.date.issued	2023-04-01
dc.identifier.citation	IEEE Transactions on Circuits and Systems II: Express Briefs, 2023, 70, (4), pp. 1660-1664
dc.identifier.issn	1549-7747
dc.identifier.issn	1558-3791
dc.identifier.uri	http://hdl.handle.net/10453/179728
dc.description.abstract	In this brief, we propose a new diffusion normalized maximum Versoria criterion (d-NMVC) algorithm, which is based on maximization of the normalized maximum Versoria criterion (MVC) cost function to enhance the performance of the distributed estimation over networks in the presence of non-Gaussian noise. Convergence of the proposed algorithm, in the mean square sense and evolution behavior, under impulsive noise environment is also analyzed. Simulation results show the robustness of the proposed algorithm under impulsive noise environment against various non-Gaussian noise distributions.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Circuits and Systems II: Express Briefs
dc.relation.isbasedon	10.1109/TCSII.2022.3230831
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject	0906 Electrical and Electronic Engineering
dc.subject.classification	Electrical & Electronic Engineering
dc.subject.classification	4006 Communications engineering
dc.subject.classification	4009 Electronics, sensors and digital hardware
dc.title	Diffusion Normalized Maximum Versoria Criterion Robust to Impulsive Noise
dc.type	Journal Article
utslib.citation.volume	70
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
utslib.copyright.status	recently_added	*
dc.date.updated	2024-07-05T01:25:39Z
pubs.issue	4
pubs.publication-status	Published
pubs.volume	70
utslib.citation.issue	4

Abstract:

In this brief, we propose a new diffusion normalized maximum Versoria criterion (d-NMVC) algorithm, which is based on maximization of the normalized maximum Versoria criterion (MVC) cost function to enhance the performance of the distributed estimation over networks in the presence of non-Gaussian noise. Convergence of the proposed algorithm, in the mean square sense and evolution behavior, under impulsive noise environment is also analyzed. Simulation results show the robustness of the proposed algorithm under impulsive noise environment against various non-Gaussian noise distributions.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/179728