Convergence Analysis of Weighted SPSA-based Consensus Algorithm in Distributed Parameter Estimation Problem. Issue 7 (2021)

• Record Type: Journal Article
• Authors: Sergeenko, Anna; Erofeeva, Victoria; Granichin, Oleg; Granichina, Olga; Proskurnikov, Anton
• Is Part Of: IFAC-PapersOnLine. Volume 54:Issue 7(2021)
• Page Start: 126
• Page End: 131
• Publication Date: 2021
• Subjects: Sensor network -- randomized algorithms -- consensus -- distributed parameter estimation; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2021.08.346
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: 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.