Robust weighted fusion steady-state Kalman estimators for systems with uncertain-variance multiplicative and additive noises
YANG Zhi-bo
DENG Zi-li
Abstract：The robust weighted fusion estimation problem is studied for the multi-sensor systems with uncertain-variance multiplica-tive and additive noises and with state-dependent and noise-dependent multiplicative noises in this paper. By introducing the fictitious noises to compensate the uncertainties of multiplicative noises,the original system is converted into one with deterministic parameters and uncertain additive noise variances. By the Lyapunov equation approach,the unified mini-max robust fusion steady-state Kalman estimators(predictor,filter and smoother)weighted by diagonal matrices are presented,where the filter and smoother are designed based on the predictor,and the minimal upper bound of actual estimation error variances of each fuser is given.It is proved that the robust ac-curacies of fusers are higher than that of each local estimator.A simulation example applied to robust fusion filtering of uninterruptible power system(UPS)shows the correctness and effectiveness of the proposed results.
Keywords：multiplicative noiseuncertain noise variancesweighted fusionmini-max robust Kalman estimatorLyapunov equation approachfictious noise approach
Publication Date：2018-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：10( 547-556 )