For linear discrete-time stochastic systems with mixed uncertainties including multiplicative and uncertain-variance linearly correlated additive white noises, this paper addresses the problem of designing robust Kalman estimators.
For linear discrete time-varying and time-invariant multisensor uncertain systems with multiplicative noises, missing measurements and uncertain-variance linearly correlated white noises, by introducing the fictitious noises to compensate the stochastic uncertainties, the system under consideration can be converted into one with only uncertain noise variances.
The uncertainties of the systems include multiplicative noises, missing measurements, and uncertain noise variances.
The uncertainties of the systems include the same multiplicative noises in state and measurement matrices, and the uncertain noise variances.
By introducing the fictitious noises, the considered system is converted into one with only uncertain noise variances.
By the fictitious noise-based Lyapunov equation approach, the system under consideration is converted into one with only uncertain noise variances.
This paper addresses the design of robust weighted fusion time-varying Kalman smoothers for multisensor time-varying system with uncertain noise variances by the augmented state approach.
By introducing the fictitious noises to compensate the multiplicative noises, the system under consideration is converted into one with only uncertain noise variances.
According to the minimax robust estimation principle, based on the worst-case system with conservative upper bounds of uncertain noise variances, the four robust weighted state fusion time-varying and steady-state Kalman estimators (predictor, filter, smoother) are presented respectively.
This paper deals with the problem of designing robust sequential covariance intersection (SCI) fusion Kalman filter for the clustering multi-agent sensor network system with measurement delays and uncertain noise variances.
