This note addresses Distributed State Estimation (DSE) over sensor networks. Two existing consensus approaches for DSE, i.e. consensus on information (CI) and consensus on measurements (CM), are combined to provide a novel class of hybrid consensus filters (named Hybrid CMCI) which enjoy the complementary benefits of CM and CI. Novel theoretical results, limitedly to linear systems, on the guaranteed stability of the Hybrid CMCI filters under collective observability and network connectivity are proved. Finally, the effectiveness of the proposed class of consensus filters is evaluated on a target tracking case-study with both linear and nonlinear sensors.

Consensus-based linear and nonlinear filtering / G. Battistelli; L. Chisci; G. Mugnai; A. Farina; A. Graziano. - In: IEEE TRANSACTIONS ON AUTOMATIC CONTROL. - ISSN 0018-9286. - STAMPA. - 60:(2015), pp. 1410-1415. [10.1109/TAC.2014.2357135]

Consensus-based linear and nonlinear filtering

BATTISTELLI, GIORGIO;CHISCI, LUIGI;MUGNAI, GIOVANNI;
2015

Abstract

This note addresses Distributed State Estimation (DSE) over sensor networks. Two existing consensus approaches for DSE, i.e. consensus on information (CI) and consensus on measurements (CM), are combined to provide a novel class of hybrid consensus filters (named Hybrid CMCI) which enjoy the complementary benefits of CM and CI. Novel theoretical results, limitedly to linear systems, on the guaranteed stability of the Hybrid CMCI filters under collective observability and network connectivity are proved. Finally, the effectiveness of the proposed class of consensus filters is evaluated on a target tracking case-study with both linear and nonlinear sensors.
2015
60
1410
1415
G. Battistelli; L. Chisci; G. Mugnai; A. Farina; A. Graziano
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/911148
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