Convergence Analysis of Weighted SPSA-based Consensus Algorithm in Distributed Parameter Estimation Problem

In this paper, we study a distributed parameter estimation problem in a large-scale network of communication sensors. The goal of the sensors is to find a global estimate of an unknown parameter minimizing, which minimizes some aggregate cost function. Each sensor can communicated to a few “neighbor...

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Bibliographic Details
Published in:IFAC-PapersOnLine 2021-01, Vol.54 (7), p.126-131
Main Authors: Sergeenko, Anna, Erofeeva, Victoria, Granichin, Oleg, Granichina, Olga, Proskurnikov, Anton
Format: Article
Language:English
Subjects:
consensus
distributed parameter estimation
randomized algorithms
Sensor network
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Summary:In this paper, we study a distributed parameter estimation problem in a large-scale network of communication sensors. The goal of the sensors is to find a global estimate of an unknown parameter minimizing, which minimizes some aggregate cost function. Each sensor can communicated to a few “neighbors”, furthermore, the communication channels have limited capacities. To solve the resulting optimization problem, we use a weighted modification of the distributed consensus-based SPSA algorithm whose main advantage over the alternative method is its ability to work in presence of arbitrary unknown-but-bounded noises whose statistical characteristics can be unknown. We provide a convergence analysis of the weighted SPSA-based consensus algorithm and show its efficiency via numerical simulations.
ISSN:2405-8963
2405-8963
DOI:10.1016/j.ifacol.2021.08.346