Centralized Fusion Estimators for Multisensor Systems With Random Sensor Delays, Multiple Packet Dropouts and Uncertain Observations

Abstract

For linear discrete-time stochastic systems measured by multiple sensors, where different sensors are subject to mixed uncertainties of random delays, packet dropouts and/or uncertain observations, the centralized fusion linear optimal estimators in the linear minimum variance sense are presented via the innovation analysis approach, which is a general and useful tool to find the optimal linear estimate. The stability of the proposed estimators is analyzed. A sufficient condition for the existence of the centralized fusion steady-state estimators is given. For a single sensor case, the proposed estimators have the simpler forms and the lower computational cost compared to the existing literature, since a lower dimension parameterized system is constructed and the colored noise is avoided. A simulation example verifies the effectiveness of the proposed estimators.