In many practical filter design problems, the exact statistical information of the underlying
random processes is not available. One robust filtering approach in these situations is to …

The recently introduced intrinsically Bayesian robust filter (IBRF) provides fully optimal
filtering relative to a prior distribution over an uncertainty class of joint random process …

Adaptive Kalman filter (AKF) is concerned with jointly estimating the system state and the
unknown parameters of the state-space models. In this paper, we treat the model uncertainty …

In many contemporary engineering problems, model uncertainty is inherent because
accurate system identification is virtually impossible owing to system complexity or lack of …

The Kalman filter is adopted in a myriad of applications for providing the minimum mean
square error estimation of time-varying parameters in a simple and systematic manner …

When designing optimal filters it is often unrealistic to assume that the statistical model is
known perfectly. The issue is then to design a robust filter that is optimal relative to an …

This paper considers the joint recursive estimation of the dynamic state and the time-varying
process noise covariance for a linear state space model. The conjugate prior on the process …

The classical Kalman smoother recursively estimates states over a finite time window using
all observations in the window. In this paper, we assume that the parameters characterizing …

Probabilistic inference is the problem of estimating the hidden variables (states or
parameters) of a system in an optimal and consistent fashion as a set of noisy or incomplete …

This paper presents a unified approach to local optimality, robustness, and Bayesian
estimation theory concepts in deriving Kalman filtering equations in the case of non …