Title :
Consensus-Based Linear and Nonlinear Filtering
Author :
Battistelli, G. ; Chisci, L. ; Mugnai, G. ; Farina, A. ; Graziano, A.
Author_Institution :
Dipt. di Ing. dell´Inf., Univ. di Firenze, Florence, Italy
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.
Keywords :
filtering theory; linear systems; observability; target tracking; collective observability; consensus-based linear filtering; consensus-based nonlinear filtering; distributed state estimation; hybrid CMCI filters; linear systems; network connectivity; target tracking; Covariance matrices; Equations; Estimation error; Kalman filters; Q measurement; Stability analysis; State estimation; Consensus; distributed state estimation; nonlinear filtering; sensor networks;
Journal_Title :
Automatic Control, IEEE Transactions on
DOI :
10.1109/TAC.2014.2357135