A Robust Adaptive Estimation Algorithm for Hamiltonian Multi-Agent Networks

In this letter a robust incremental adaptation algorithm is presented to solve distributed estimation for a Hamiltonian network, where the measurements at each node may be corrupted by heavy-tailed impulsive noise. In the proposed algorithm, each node employs an error-nonlinearity into the update eq...

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Bibliographic Details
Published in:IEEE control systems letters 2021-10, Vol.5 (4), p.1243-1248
Main Authors: Giv, Hoorie, Khalili, Azam, Rastegarnia, Amir, Sanei, Saeid
Format: Article
Language:English
Subjects:
Acoustic measurements
Adaptive network
Adaptive systems
Estimation
Hamilton
incremental
Mathematical model
Noise measurement
robust estimation
Robustness
Steady-state
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Summary:In this letter a robust incremental adaptation algorithm is presented to solve distributed estimation for a Hamiltonian network, where the measurements at each node may be corrupted by heavy-tailed impulsive noise. In the proposed algorithm, each node employs an error-nonlinearity into the update equation to mitigate the detrimental effects of impulsive noise. Moreover, the algorithm estimates both the optimal error non-linearity and the unknown parameter together, which in turn, obviates the requirement of prior knowledge about the statistical characteristics of measurement noise. In addition to algorithm development, its steady-state performance as well as convergence analysis have been provided. Simulation results validate the correctness of the analysis and reveal the superiority of the proposed algorithm over some existing algorithms.
ISSN:2475-1456
2475-1456
DOI:10.1109/LCSYS.2020.3029332