Distributed Adaptive Filtering of α-Stable Signals

Sayed Pouria Talebi, Stefan Werner, Danilo Mandic

Research output: Contribution to journalArticleScientificpeer-review

23 Citations (Scopus)
168 Downloads (Pure)


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 languageEnglish
Pages (from-to)1450 - 1454
Number of pages5
JournalIEEE Signal Processing Letters
Issue number10
Publication statusPublished - Oct 2018
MoE publication typeA1 Journal article-refereed


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


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