The paper addresses Distributed State Estimation (DSE) over sensor networks. Two existing consensus approaches for DSE of linear systems, named consensus on information (CI) and consensus on measurements (CM), are extended to nonlinear systems. Further, a novel hybrid consensus approach exploiting both CM and CI (named HCMCI=Hybrid CM + CI) is introduced in order to combine their complementary benefits. Novel theoretical results, limitedly to linear systems, on the guaranteed stability of the HCMCI filter under minimal requirements (i.e. collective observability and network connectivity) are proved. Finally, a simulation case-study is presented in order to comparatively show the effectiveness of the proposed consensus-based state estimators.
Consensus-based algorithms for distributed filtering / G. Battistelli; L. Chisci; G. Mugnai; A. Farina; A. Graziano. - STAMPA. - (2012), pp. 794-799. (Intervento presentato al convegno 51st IEEE Conference on Decision and Control tenutosi a Maui, USA) [10.1109/CDC.2012.6426435].
Consensus-based algorithms for distributed filtering
BATTISTELLI, GIORGIO;CHISCI, LUIGI;MUGNAI, GIOVANNI;
2012
Abstract
The paper addresses Distributed State Estimation (DSE) over sensor networks. Two existing consensus approaches for DSE of linear systems, named consensus on information (CI) and consensus on measurements (CM), are extended to nonlinear systems. Further, a novel hybrid consensus approach exploiting both CM and CI (named HCMCI=Hybrid CM + CI) is introduced in order to combine their complementary benefits. Novel theoretical results, limitedly to linear systems, on the guaranteed stability of the HCMCI filter under minimal requirements (i.e. collective observability and network connectivity) are proved. Finally, a simulation case-study is presented in order to comparatively show the effectiveness of the proposed consensus-based state estimators.
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