System state estimation in the presence of noise is critical for control systems, signal
processing, and many other applications in a variety of fields. Developed decades ago, the …

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 …

The Kalman filter is a well-known recursive state estimator for linear systems. In practice, the
algorithm is often used for non-linear systems by linearizing the system's process and …

This paper describes an algorithm that identifies a state-space model and an associated
steady-state Kalman filter gain from noise-corrupted input–output data. The model structure …

The Kalman filter is an established tool for the analysis of dynamic systems with normally
distributed noise, and it has been successfully applied in numerous areas. It provides …

Kalman filtering algorithm gives optimal (linear, unbiased and minimum error-variance)
estimates of the unknown state vectors of a linear dynamic-observation system, under the …

As a recursive state estimation algorithm, the Kalman filter (KF) assumes initial state
distribution is known a priori, while in practice the initial distribution is commonly treated as …

In this paper, we discuss the Kalman filter for state estimation in noisy linear discrete-time
dynamical systems. We give an overview of its history, its mathematical and statistical …

The Kalman filter is a powerful state estimator and has been successfully applied in many
fields. To guarantee the optimality of the Kalman filter, the noise covariances need to be …

The input-parameter-state estimation capabilities of a novel unscented Kalman filter is
examined herein on both linear and nonlinear systems. The unknown input is estimated in …