Remote State Estimation With Enhanced Robustness in the Presence of Data Packet Dropouts

Abstract

This article discusses the remote state estimation over unreliable links, where the packet dropouts occur from the sensor side to the filter, for discrete-time systems with both bounded-power disturbances and white Gaussian noises. A cascaded estimation scheme with enhanced robustness is proposed, driven by the residual signal related to the modeling mismatch. The estimation gains are characterized by two modified algebraic Riccati equations (MAREs), together with a complete and rigorous stability analysis in the mean square (MS) sense. Necessary and sufficient conditions for the MS stability of the corresponding error dynamics are then given in terms of the data arrival rate and unstable poles of the plant, i.e., the Mahler measure of the plant. Moreover, the filtering strategy is expanded into the case of distributed estimation over lossy sensor networks, where each sensor locally constructs an estimate based on its own observation and on those collected from its neighbors, and the solution is again derived by MAREs. The corresponding necessary and sufficient conditions to the MS stability in the distributed case are also characterized by relationships between data arrival rates and the Mahler measure of the plant. Finally, an example is presented to validate the current design method.