The methods of the class of Kalman filters have recently been used in the estimation of the term structure of interest rates. These methods can employ both time-series and cross-sectional aspects of term structure models. This paper compares the performance of two kinds of non-linear Kalman filter algorithms Extended Kalman Filter (EKF) and Square-Root Unscented Kalman Filter (SRUKF) in estimat...

The traditional Extended Kalman filter (EKF) is a useful tool for structural parameter identification with limited observations. It is, however, not applicable when the excitations on the structure are unknown or the excitation locations are not monitored. A novel Extended Kalman filter approach referred to as the General Extended Kalman filter with unknown inputs (GEKF-UI) is proposed to estim...

Over the last 20-30 years, the extended Kalman filter (EKF) has become the algorithm of choice in numerous nonlinear estimation and machine learning applications. These include estimating the state of a nonlinear dynamic system as well estimating parameters for nonlinear system identification (e.g., learning the weights of a neural network). The EKF applies the standard linear Kalman filter met...

the radar tracking is one of the best leo satellite tracking methods. while the tracking filters which are mostly linear, and them are not able to have a precise estimation of the objects with nonlinear motion dynamic such as satellite, we should use nonlinear filters. in this paper , firstly, we deal with the problem of the leo satellites motion path modeling according to the satellite motion ...

This paper describes a new approach for generalizing the Kalman filter to nonlinear systems. A set of samples are used to parameterize the mean and covariance of a (not necessarily Gaussian) probability distribution. The method yields a filter that is more accurate than an extended Kalman filter (EKF) and easier to implement than an EKF or a Gauss second-order filter. Its effectiveness is demon...

Kalman filters provide an important technique for estimating the states of engineering systems. With several variations of nonlinear Kalman filters, there is a lack of guidelines for filter selection with respect to a specific research or engineering application. This creates a need for an in-depth discussion of the intricacies of different nonlinear Kalman filters. Particularly of interest for...

Different approaches for the estimation of the states of linear dynamic systems are commonly used, the most common being the Kalman filter. For nonlinear systems, variants of the Kalman filter are used. Some of these variants include the LKF (linearized Kalman filter), the EKF (extended Kalman filter), and the UKF (unscented Kalman filter). With the LKF and EKF, performance varies depending on ...

A new Gaussian mixture filter has been developed, one that uses a re-sampling step in order to limit the covariances of its individual Gaussian components. The new filter has been designed to produce accurate solutions of difficult nonlinear/nonBayesian estimation problems. It uses static multiple-model filter calculations and Extended Kalman Filter (EKF) approximations for each Gaussian mixand...

this paper presents a new multi-sensor data fusion method based on the combination of wavelettransform (wt) and extended kalman filter (ekf). input data are first filtered by a wavelettransform via daubechies wavelet “db4” functions and the filtered data are then fused based onvariance weights in terms of minimum mean square error. the fused data are finally treated byextended kalman filter for...