Open AccessEvent‐triggered Kalman consensus filter over sensor networks
Author(s) -
Li Wenling,
Jia Yingmin,
Du Junping
Publication year - 2016
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2015.0508
Subject(s) - kalman filter , control theory (sociology) , transmission (telecommunications) , wireless sensor network , event (particle physics) , computer science , data transmission , scalability , protocol (science) , filter (signal processing) , mathematics , lyapunov stability , artificial intelligence , telecommunications , computer network , physics , control (management) , quantum mechanics , medicine , alternative medicine , pathology , database , computer vision
Kalman consensus filter (KCF) has been developed for distributed state estimation over sensor networks where local estimates are exchanged with time‐triggered transmission mechanism. To reduce the amount of data transfer in sensor networks, the authors propose a KCF with an event‐triggered communication protocol. The triggering decision is based on the send‐on‐delta data transmission mechanism: each sensor transmits its local estimates to its neighbours only if the difference between the most recent transmitted estimate and the current estimate exceeds a tolerable threshold. On the basis of the event‐triggered communication protocol, an optimal Kalman gain matrix is derived by minimising the mean squared errors for each sensor and a suboptimal KCF is developed for scalable considerations. By using the Lyapunov‐based approach, a sufficient condition is presented for ensuring the stochastic stability of the suboptimal KCF. A numerical example is provided to verify the effectiveness of the proposed filter.