State estimation in the presence of non-Gaussian noise is discussed. Since the Kalman filter
uses only second-order signal information, it is not optimal in non-Gaussian noise …

To date, most linear and nonlinear Kalman filters (KFs) have been developed under the
Gaussian assumption and the well-known minimum mean square error (MMSE) criterion. In …

Most distributed Kalman filters are based on the cost function of the well-known minimum
mean square estimation criterion, which performs well in the presence of Gaussian noise …

As a nonlinear extension of Kalman filter, the extended Kalman filter (EKF) is also based on
the minimum mean square error (MMSE) criterion. In general, the EKF performs well in …

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 …

For linear systems, the original Kalman filter under the minimum mean square error (MMSE)
criterion is an optimal filter under a Gaussian assumption. However, when the signals follow …

In the Kalman filter (KF), the estimated state is a linear combination of the one-step
prediction and measurement. The two combination weights depend on the prediction mean …

The extended Kalman filter (EKF) is a method extensively applied in many areas,
particularly, in nonlinear target tracking. The optimization criterion commonly used in EKF is …

Abstract The Unscented Kalman filter (UKF) based on maximum correntropy criterion (MCC)
is robust to heavy-tailed non-Gaussian noise. However, the approximate linear …

The Hilbert space embedding provides a powerful and flexible tool for dealing with the
nonlinearity and high-order statistics of random variables in a dynamical system. The kernel …