Covariance Intersection Fusion Robust Steady-State Kalman Filter for Two-Sensor Systems with Time-Delayed Measurements

For two-sensor systems with time-delayed measurements and uncertain noise variances, this paper presents a measurements transformation approach which transforms the systems with time-delayed measurements into the equivalent systems without measurement delays. Further the local robust steady-state Kalman filter with conservative upper bounds of unknown noise variances is presented, and then the covariance intersection (CI) fusion robust steady-state Kalman filter is also presented. The robustness of these filters is proved based on the Lyapunov equation. It is proved that the robust accuracy of the CI fuser is higher than that of each local robust Kalman filter. A Monte-Carlo simulation example shows its correctness and effectiveness.