Distributed asynchronous consensus-based algorithm for blind calibration of sensor networks with autonomous gain correction

In this study, a new algorithm is proposed for distributed asynchronous consensus-based blind calibration of sensor networks with noisy communications and measurements. The algorithm consists of one autonomous recursion of the instrumental variable type for gain correction and one additional recursi...

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Bibliographic Details
Published in:IET control theory & applications Vol. 12; no. 16; pp. 2287 - 2293
Main Author: Stanković, Maja
Format: Journal Article
Language:English
Published: The Institution of Engineering and Technology 06.11.2018
Subjects:
additional recursion
approximation theory
asymptotic consensus
asynchronous stochastic approximation arguments
autonomous gain correction
autonomous recursion
blind macro‐calibration
Brief Paper
calibration
convergence
corrected gains
corrected sensor gains
distributed asynchronous consensus‐based algorithm
distributed asynchronous consensus‐based blind calibration
existing similar algorithms
gradient type
instrumental variable type
least squares approximations
mean square error methods
measurements
noisy communications
offset correction
offsets
probability
sensor networks
stochastic processes
wireless sensor networks
distributed asynchronous consensus-based blind calibration
wireless sensor networks
convergence
gradient type
sensor networks
asymptotic consensus
instrumental variable type
asynchronous stochastic approximation arguments
stochastic processes
calibration
mean square error methods
existing similar algorithms
measurements
approximation theory
least squares approximations
additional recursion
probability
autonomous gain correction
corrected gains
autonomous recursion
blind macro-calibration
distributed asynchronous consensus-based algorithm
noisy communications
offsets
offset correction
corrected sensor gains
ISSN:1751-8644, 1751-8652
Online Access:Get full text
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Description
Summary:In this study, a new algorithm is proposed for distributed asynchronous consensus-based blind calibration of sensor networks with noisy communications and measurements. The algorithm consists of one autonomous recursion of the instrumental variable type for gain correction and one additional recursion of gradient type for offset correction based on the corrected gains. It is proved using asynchronous stochastic approximation arguments that the algorithm achieves asymptotic consensus with regard to both the corrected sensor gains and offsets in the mean square sense and with probability one. The algorithm is more flexible than the existing similar algorithms for blind macro-calibration and provides a superior convergence rate, especially when used in networks with one fixed reference node. Simulation results confirm the main theoretical statements.
ISSN:1751-8644
1751-8652
DOI:10.1049/iet-cta.2018.5417