Conventional Kalman filter (KF) relies heavily on a priori knowledge of the potentially
unstable process and measurement noise statistics. Insufficiently known a priori filter …

There are different ways to construct adaptive Kalman filtering (AKF) algorithms. This paper
proposes an innovative way to simultaneously estimate the variance matrix R of the …

This paper preliminarily investigates the application of unscented Kalman filter (UKF)
approach with nonlinear dynamic process modeling for Global positioning system (GPS) …

The Global Positioning System (GPS) is a satellite-based navigation system that provides a
user with the proper equipment access to useful and accurate positioning information …

Given that the traditional Kalman filtering algorithm is difficult to catch the characteristics of
dynamic noise and observation noise in the application of vehicular GPS dynamic …

Abstract Integrated GPS/INS using Kalman filter is the best technique for improving
navigation accuracy. Assuming that the covariance matrices are known and constant, a …

Standard Kalman Filtering leads to divergence because of inaccurate system model and
noise statistic. Researchers have taken relative studies about Kalman filtering optimization …

The classical unscented Kalman filter (UKF) requires prior knowledge on statistical
characteristics of system noises for state estimation of a nonlinear dynamic system. If the …

A generalized Kalman filtering estimator with nonlinear models is derived based on
correlational inference, in which a new target function with constraint equation is …

It is well known that the uncertainty of the covariance parameters of the process noise (Q)
and the observation errors (R) has a significant impact on Kalman filtering performance. Q …