Remote State Estimation for Jump Markov Nonlinear Systems: A Stochastic Event-Triggered Approach

Abstract

This paper investigates the remote state estimation issue for the jump Markov nonlinear systems (JMNLSs) with the stochastic event-triggered transmission strategy. For the purpose of saving the scarce network resources, the stochastic event-triggered communication is employed to cut down the number of measurement transmission. The interacting multiple model (IMM) scheme is incorporated due to its strength in alleviating computational burden encountered in the multiple model state estimation problem. In addition, the estimated measurement is utilized to update the mode probability in IMM-based filter when the current measurement is not available to the remote estimators. The proposed algorithm is applied in a two-dimensional maneuvering target tracking problem and the simulation results are presented, which validates the usefulness of the developed scheme.