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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 |
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Pages (from-to) | 1450 - 1454 |
Number of pages | 5 |
Journal | IEEE Signal Processing Letters |
Volume | 25 |
Issue number | 10 |
DOIs | |
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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Projects
- 1 Finished
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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