Scalable event-triggered distributed extended Kalman filter for nonlinear systems subject to randomly delayed and lost measurements

摘要: Abstract In this paper, a distributed extended Kalman filter (EKF) is developed for class of nonlinear systems, whose outputs are measured by multiple sensors which send data using an event triggered mechanism through communication network subject to loss and latency. Random transmission delay dropouts modelled Bernoulli random sequence. The gains determined in each sensor node such that upper bound on the cross covariance estimation error minimized; so, less computational burden required, even networks with large number nodes. To be specific, scalability main feature proposed scheme. boundedness filtering proved under some conditions. Finally, comparative simulation results presented illustrate effectiveness applicability suggested filter.