The paper addresses discrete-time event-driven con- sensus on exponential-class probability densities (including Gaus- sian, binomial, Poisson, Rayleigh, Wishart, Inverse Wishart and many other distributions of interest) completely specified by a finite-dimensional vector of so called natural parameters. First, it is proved how such exponential classes are closed under Kullback- Leibler fusion (average), and how the latter is equivalent to a weighted arithmetic average over the natural parameters. Then, a novel event-driven transmission strategy is proposed so as to tradeoff data communication rate, and hence energy consump- tion, versus consensus speed and accuracy. A theoretical analysis of the convergence properties of the proposed algorithm is provided by exploiting the Fisher metric as a local approximation of the Kullback-Leibler divergence. Some numerical examples are presented in order to demonstrate the effectiveness of the proposed event-driven consensus. It is expected that the latter can be successfully exploited for energy- and/or bandwidth-efficient networked state estimation.

Distributed averaging of exponential-class densities with discrete-time event-triggered consensus / Battistelli, Giorgio; Chisci, Luigi; Selvi, Daniela. - In: IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS. - ISSN 2325-5870. - STAMPA. - 5:(2018), pp. 359-369. [10.1109/TCNS.2016.2611384]

Distributed averaging of exponential-class densities with discrete-time event-triggered consensus

BATTISTELLI, GIORGIO;CHISCI, LUIGI;SELVI, DANIELA
2018

Abstract

The paper addresses discrete-time event-driven con- sensus on exponential-class probability densities (including Gaus- sian, binomial, Poisson, Rayleigh, Wishart, Inverse Wishart and many other distributions of interest) completely specified by a finite-dimensional vector of so called natural parameters. First, it is proved how such exponential classes are closed under Kullback- Leibler fusion (average), and how the latter is equivalent to a weighted arithmetic average over the natural parameters. Then, a novel event-driven transmission strategy is proposed so as to tradeoff data communication rate, and hence energy consump- tion, versus consensus speed and accuracy. A theoretical analysis of the convergence properties of the proposed algorithm is provided by exploiting the Fisher metric as a local approximation of the Kullback-Leibler divergence. Some numerical examples are presented in order to demonstrate the effectiveness of the proposed event-driven consensus. It is expected that the latter can be successfully exploited for energy- and/or bandwidth-efficient networked state estimation.
2018
5
359
369
Battistelli, Giorgio; Chisci, Luigi; Selvi, Daniela
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1075125
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