Modified robust covariance intersection fusion steady-state Kalman filter for systems with missing measurements and uncertain noise variances
WANG Xue-mei
LIU Wen-qiang
DENG Zi-li
Abstract：For the linear time-invariant multisensor system with missing measurements and uncertain noise variances, by introducing the fictitious noises, the original system can be converted into one with only uncertain noise variances. According to the minimax robust estimation principle, using the Lyapunov equation approach, the local robust steady-state Kalman filters and the minimal upper bounds of their actual variances are presented, and a modified robust covariance intersection (CI) fusion steady-state Kalman filter and the minimal upper bound of its actual variances are presented using the conservative cross-covariances of the local filtering errors. The robustness of the robust local and fused filters is proved, and it is proved that the robust accuracy of the modified CI fuser is higher than that of the original CI fuser, and higher than that of each local filter. A simulation example verifies correctness and effectiveness of the proposed results.
Keywords：multisensor systemuncertain noise variancemissing measurementscovariance intersection (CI) fusionminimax robust Kalman filterLyapunov equation approach
Publication Date：2016-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 973-979 )