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

Abstract

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

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