dc.contributor.author | Gogineni, Vinay Chakravarthi | |
dc.contributor.author | Werner, Stefan | |
dc.date.accessioned | 2023-01-27T07:22:49Z | |
dc.date.available | 2023-01-27T07:22:49Z | |
dc.date.created | 2022-10-20T03:53:21Z | |
dc.date.issued | 2020 | |
dc.identifier.issn | 1070-9908 | |
dc.identifier.uri | https://hdl.handle.net/11250/3046731 | |
dc.description.abstract | This work revisits the problem of distributed adaptive filtering in multi-agent sensor networks. In contrast to classical approaches, the formulation relaxes the Gaussian assumption on the signal and noise to the generalized setting of α-stable distributions that do not possess second- and higher-order statistical moments. Most importantly, the considered scenario allows for different characteristic exponents throughout the network. Drawing upon ideas from correntropy-type local similarity measures and fractional-order calculus, a novel class of distributed fractional-order correntropy adaptive filters, that are robust against the jittery behavior of α-stable signals, is derived and their convergence criterion is established. The effectiveness of the proposed algorithms, as compared to existing distributed adaptive filtering techniques, is demonstrated via simulation examples. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | IEEE | en_US |
dc.title | Fractional-Order Correntropy Adaptive Filters for Distributed Processing of α -Stable Signals | en_US |
dc.title.alternative | Fractional-Order Correntropy Adaptive Filters for Distributed Processing of α -Stable Signals | en_US |
dc.type | Journal article | en_US |
dc.type | Peer reviewed | en_US |
dc.description.version | acceptedVersion | en_US |
dc.rights.holder | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_US |
dc.source.journal | IEEE Signal Processing Letters | en_US |
dc.identifier.doi | 10.1109/LSP.2020.3029702 | |
dc.identifier.cristin | 2062999 | |
cristin.ispublished | true | |
cristin.fulltext | postprint | |
cristin.qualitycode | 1 | |