Modeling and filtering for a stochastic uncertain system in a complex scenario

This paper presents a new approach to filter signals for discrete-time physical problems with stochastic uncertain in the presence of random data transmission delays, out-of-order packets and correlated noise. To deal packet disorder, system model synthesizing delays from plant is established by utilizing signal reconstruction schemes based on zero-order-holder logic zero-order-holder. A robust finite horizon Kalman proposed augmenting state-space minimizing error covariance. further improve filtering performance, linear estimation-based delay compensation strategy employing reorganized time-stamped measurements. Moreover, solving missing measurement problem whilst reducing computational costs, an artificial using one-step prediction approach. Simulation results show effectiveness method.