Distributed Adaptive Filtering of α-Stable Signals

Sayed Pouria Talebi; Stefan Werner; Danilo Mandic

doi:10.1109/LSP.2018.2862639

Distributed Adaptive Filtering of α-Stable Signals

Sayed Pouria Talebi, Stefan Werner, Danilo Mandic

Department of Signal Processing and Acoustics

Research output: Contribution to journal › Article › Scientific › peer-review

15 Citations (Scopus)

149 Downloads (Pure)

Abstract

A cost-effective framework for distributed filtering of α-stable signals over sensor networks is proposed. To this end, the problem of filtering α-stable signals through multiple observations made over a network of sensors is revisited and an optimal solution is formulated. Then, an adaptive gradient descent based algorithm for distributed real-time filtering of α-stable signals via multi-agent networks is derived. The derived algorithm not only gives an approximation of the formulated optimal solution, but is also cost-effective and scalable with the size of the network. Moreover, performance of the derived algorithm is analyzed and convergence conditions are established.

Original language	English
Pages (from-to)	1450 - 1454
Number of pages	5
Journal	IEEE Signal Processing Letters
Volume	25
Issue number	10
DOIs	https://doi.org/10.1109/LSP.2018.2862639
Publication status	Published - Oct 2018
MoE publication type	A1 Journal article-refereed

Keywords

α-stable random signals
consensus fusion
distributed adaptive filtering
fractional differential
Sensor networks

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10.1109/LSP.2018.2862639

ELEC-Talebi-et-al-Distributed Adaptive Filtering of Stable SignalsAccepted author manuscript, 548 KB

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1 Finished

Robust Demand-End Optimization with Event-Triggered Situational Awareness
Leithon , J., Talebi, P., Werner, S., Riihonen, T. & Abedi, M.
01/09/2016 → 31/12/2020
Project: Academy of Finland: Other research funding

Cite this

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title = "Distributed Adaptive Filtering of α-Stable Signals",

abstract = "A cost-effective framework for distributed filtering of α-stable signals over sensor networks is proposed. To this end, the problem of filtering α-stable signals through multiple observations made over a network of sensors is revisited and an optimal solution is formulated. Then, an adaptive gradient descent based algorithm for distributed real-time filtering of α-stable signals via multi-agent networks is derived. The derived algorithm not only gives an approximation of the formulated optimal solution, but is also cost-effective and scalable with the size of the network. Moreover, performance of the derived algorithm is analyzed and convergence conditions are established.",

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author = "Talebi, {Sayed Pouria} and Stefan Werner and Danilo Mandic",

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AB - A cost-effective framework for distributed filtering of α-stable signals over sensor networks is proposed. To this end, the problem of filtering α-stable signals through multiple observations made over a network of sensors is revisited and an optimal solution is formulated. Then, an adaptive gradient descent based algorithm for distributed real-time filtering of α-stable signals via multi-agent networks is derived. The derived algorithm not only gives an approximation of the formulated optimal solution, but is also cost-effective and scalable with the size of the network. Moreover, performance of the derived algorithm is analyzed and convergence conditions are established.

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Distributed Adaptive Filtering of α-Stable Signals

Abstract

Keywords

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Robust Demand-End Optimization with Event-Triggered Situational Awareness

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