Adaptive Kalman filtering in networked systems with random sensor delays, multiple packet dropouts and missing measurements

In this paper, adaptive filtering schemes are proposed for state estimation in sensor networks and/or networked control systems with mixed uncertainties of random measurement delays, packet dropouts and missing measurements. That is, all three uncertainties in the measurement have certain probability of occurrence in the network. The filter gains can be derived by solving a set of recursive discrete-time Riccati equations. Examples are presented to demonstrate the applicability and performances of the proposed schemes.