Covariance intersection fusion robust steady-state kalman predictor for two-sensor systems with unknown noise variances

Abstract

For two-sensor systems with uncertainties of noise variances, a local robust steady-state Kalman one-step and multi-step predictors with the minimum upper bounds variances are presented respectively. Their robustness is proved based on the Lyapunov equation. Further, the covariance intersection (CI) fusion robust steady-state Kalman predictors are also presented by the convex combination of the local robust Kalman predictors. It is proved that its robust accuracy is higher than that of each local robust Kalman predictor. A Monte-Carlo simulation example shows its correctness and effectiveness. © 2013 Springer-Verlag Berlin Heidelberg.