In this paper, the unscented Kalman filtering (UKF) problem is investigated for a class of
general nonlinear systems with stochastic uncertainties under communication protocols. A …

The performance of the modified unscented Kalman filter (UKF) for nonlinear stochastic
discrete-time system with linear measurement equation is investigated. It is proved that …

In this paper, the state estimation problem is investigated for a class of discrete nonlinear
systems via sensor networks. A novel robust distributed extended Kalman filter, which can …

The conventional unscented Kalman filter (UKF) requires prior knowledge on system noise
statistics. If the statistical characteristics of system noise are not known exactly, the filtering …

The normal unscented Kalman filter (UKF) suffers from performance degradation and even
divergence while mismatch between the noise distribution assumed to be known as a priori …

Unscented Kalman filter (UKF) has been extensively used for state estimation of nonlinear
stochastic systems, which suffers from performance degradation and even divergence when …

Traditional Kalman filter (KF) is derived under the well-known minimum mean square error
(MMSE) criterion, which is optimal under Gaussian assumption. However, when the signals …

In this paper, we propose a systematization of the (discrete-time) Unscented Kalman Filter
(UKF) theory. We gather all available UKF variants in the literature, present corrections to …

The unscented Kalman filter (UKF) is a promising approach for the state estimation of
nonlinear dynamic systems due to its simple calculation process and superior performance …

In this paper, the extended Kalman filtering problem is investigated for a class of nonlinear
systems with multiple missing measurements over a finite horizon. Both deterministic and …