Kullback–Leibler average, consensus on probability densities, and distributed state estimation with guaranteed stability

This paper addresses distributed state estimation over a sensor network wherein each node–equipped with processing, communication and sensing capabilities–repeatedly fuses local information with information from the neighbors. Estimation is cast in a Bayesian framework and an information-theoretic a...

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Veröffentlicht in:	Automatica (Oxford) Jg. 50; H. 3; S. 707 - 718
Hauptverfasser:	Battistelli, Giorgio, Chisci, Luigi
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Kidlington Elsevier Ltd 01.03.2014 Elsevier
Schlagworte:	Applied sciences Computer science; control theory; systems Computer systems and distributed systems. User interface Consensus filters Control theory. Systems Data processing. List processing. Character string processing Distributed state estimation Exact sciences and technology Information retrieval. Graph Memory organisation. Data processing Modelling and identification Networked estimation Sensor networks Software Theoretical computing Sensor networks Distributed state estimation Networked estimation Consensus filters Density estimation Density measurement Error estimation Probability density Relative entropy Graph connectivity Observer Probability density function System identification State estimation Observability Bayes estimation Capability index Estimation error Stability Network flow Computer simulation Interconnected power system Distributed system Consensus Requirement Linear system Filter Data fusion Sensor array Information theory
ISSN:	0005-1098, 1873-2836
Online-Zugang:	Volltext
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