Model assimilation of data strives to determine optimally the state of an evolving physical system from a limited number of observations. The present study represents the first attempt of applying the extended Kalman filter (EKF) method of data assimilation to shock-wave dynamics induced by a high-speed impact. EKF solves the full nonlinear state evolution and estimates its associated error-cov...

The extended Kalman lter (EKF) can be used as an on-line algorithm to determine the weights in a recurrent network given target outputs as it runs. This paper notes some relationships between the EKF as applied to recurrent net learning and some simpler techniques that are more widely used. In particular, making certain simpliications to the EKF gives rise to an algorithm essentially identical ...

In a GPS/IMU tightly-coupled navigation system, the extended Kalman filter (EKF) is widely used to estimate the navigation states, due to its simpler implementation and lower computational load. However, the EKF is a first order approximation to the nonlinear system. When the nonlinearity of the system is high, the negligibility in higher order terms of the nonlinear system will degrade the est...

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 ...

To assist nature observation, we take on the challenge of detecting the species of a flying bird using a single camera. We study the bird flying data and find that a bird body axis is an invariant dimension during flight. We then develop a model-based detection approach that verifies the body axis information with the known bird flying dynamics. As a commonly used method, an extended Kalman fil...

This paper proposes a new adaptive extended Kalman filter (AEKF) for a class of nonlinear systems perturbed by noise which is not necessarily additive. The proposed filter is adaptive against the uncertainty in the process and measurement noise covariances. This is accomplished by deriving two recursive updating rules for the noise covariances, these rules are easy to implement and reduce the n...

extended kalman filtering (ekf) is a nonlinear dynamic data reconciliation (nddr) method. one of its main advantages is its suitability for on-line applications. this paper presents an on-line nddr method using ekf. it is implemented for two case studies, temperature measurements of a distillation column and concentration measurements of a cstr. in each time step, random numbers with zero mean ...

This paper evaluates the positioning and tracking performance of Extended Kalman Filter (EKF) in wireless sensors network. The EKF is a linear approximation of statistical Kalman Filter (KF) and has the capability to work efficiently in nonlinear systems. The EKF is based on an iterative process of estimating current state information from the previously estimated state. Its working is based on...

Model assimilation of data strives to determine optimally the state of an evolving physical system from a limited number of observations. The present study represents the first attempt of applying the extended Kalman filter (EKF) method of data assimilation to shock-wave dynamics induced by a high-speed impact. EKF solves the full nonlinear state evolution and estimates its associated error-cov...

[1] An Extended Kalman Filter (EKF) for the assimilation of remotely sensed nearsurface soil moisture into the Interactions between Surface, Biosphere, and Atmosphere (ISBA) model is described. ISBA is the land surface scheme in Météo-France’s Aire Limitée Adaptation Dynamique développement InterNational (ALADIN) Numerical Weather Prediction (NWP) model, and this work is directed toward providi...