Since the landmark work of RE Kalman in the 1960s, considerable efforts have been
devoted to time series state space models for a large variety of dynamic estimation …

The Kalman filter is the minimum-variance state estimator for linear dynamic systems with
Gaussian noise. Even if the noise is non-Gaussian, the Kalman filter is the best linear …

In this paper, an event-triggered near optimal control structure is developed for nonlinear
continuous-time systems with control constraints. Due to the saturating actuators, a …

The real-world applications in signal processing generally involve estimating the system
state or parameters in nonlinear, non-Gaussian dynamic systems. The estimation problem …

This paper presents a robust dynamic state estimator for the synchronous generators with
unknown parameters. The estimator uses a constrained iterated unscented Kalman filter to …

SUMMARY A new framework for estimation of lumped (for instance, Windkessel) model
parameters from uncertain clinical measurements is presented. The ultimate aim is to …

Gait can reflect locomotion and physical condition and thus is used to assess people's
health. Traditional high-precision gait analysis devices are expensive and are limited to …

This paper compares state estimation techniques for nonlinear stochastic dynamic systems,
which are important for target tracking. Recently, several methods for nonlinear state …

This paper is concerned with the gain-constrained recursive filtering problem for a class of
time-varying nonlinear stochastic systems with probabilistic sensor delays and correlated …

Inverse problems are common and important in many applications in computational physics
but are inherently ill-posed with many possible model parameters resulting in satisfactory …