As Kalman filter technology has better performance for estimation and prediction of dynamic signal, it is gradually used in GNSS signal tracking. According to the steady-state error, transfer function and equivalent noise bandwidth of Kalman filter and traditional loop in steady status, the tracking performance of these two methods is compared in theory. The theoretical analysis demonstrates th...

The goal of this study is to compare the performances of the ensemble Kalman filter and a reduced-rank extended Kalman filter when applied to different dynamic regimes. Data assimilation experiments are performed using an eddy-resolving quasi-geostrophic model of the winddriven ocean circulation. By changing eddy viscosity, this model exhibits two qualitatively distinct behaviors: strongly chao...

This paper addresses the problem of the odometry error estimation during the robot navigation. The robot is equipped with an external sensor (like laser range finder). Concerning the systematic error an augmented Kalman Filter is introduced. This filter estimates a vector state containing the robot configuration and the parameters characterizing the systematic component of the odometry error. I...

In this paper, after defining pure proportional navigation guidance in the 3-dimensional state from a new point of view, range estimation for passive homing missiles isexplained. Modeling has been performed by using line of sight coordinates with a particulardefinition. To obtain convergent estimates of those state variables involved particularly inrange channel and unavailable from IR trackers...

In this contribution we extend Kalman-filter theory by introducing a new recursive linear minimum mean squared error (MMSE) filter for dynamic systems with unknown state-vector means. The recursive filter enables the joint MMSE prediction and estimation of the random state vectors and their unknown means, respectively. We show how the new filter reduces to the Kalman-filter in case the state-ve...

The Kalman Bucy filter is a well-known observer to estimate the state vector from the incomplete state measurements. However, when the time delay is taken into account, the filter can become ineffective. In this paper, the identification algorithm presented in a previous paper (Anh 2000) is used to improve the Kalman Bucy filter in the presence of time delay. The differential equation of the ob...

Background: The Kalman filter is an effective tool for estimating a stochastic process in noise. Given a state-space model of a stochastic process, the Kalman filter recursively estimates state variables from noisy measurements by minimizing the sum of squared estimation errors. It is an optimal estimator for a Gauss random process and provides estimates of error variances. Its application for ...

For parameter estimations, we have developed a extended kalman filter(EKF) and unscented kalman filter(UKF) for linear as well as non-linear spacecraft systems. We have described the differences of two approaches mathematical equation for modeling of the system value of mean square error, error covariance and For state estimation of satellite, two different type of filters has been described i....

The article addresses the issue of mobile robotic platform positioning in GNSS-denied environments real-time. proposed system relies on fusing data from an Inertial Measurement Unit (IMU), magnetometer, and encoders. To get symmetrical error gauss distribution for measurement model achieve better performance, Error-state Extended Kalman Filter (ES EKF) is chosen. There are two stages vector sta...