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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Veröffentlicht in:	IET control theory & applications Jg. 12; H. 16; S. 2287 - 2293
1. Verfasser:	Stanković, Maja
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	The Institution of Engineering and Technology 06.11.2018
Schlagworte:	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
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Zusammenfassung:	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