in the several past years, extended kalman filter (ekf) and unscented kalman filter (ukf) havebecame basic algorithm for state-variables and parameters estimation of discrete nonlinear systems.the ukf has consistently outperformed for estimation. sometimes least estimation error doesn't yieldwith ukf for the most nonlinear systems. in this paper, we use a new approach for a two variablesta...

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...

In the several past years, Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) havebecame basic algorithm for state-variables and parameters estimation of discrete nonlinear systems.The UKF has consistently outperformed for estimation. Sometimes least estimation error doesn't yieldwith UKF for the most nonlinear systems. In this paper, we use a new approach for a two variablestate no...

State estimation is the common problem in every area of engineering. There are different filters used to overcome the problem of state estimation like Kalman filter, Particle filters etc. Kalman Filter is popular when the system is linear but when the system is highly non-linear then the different derivatives of Kalman Filter are used like Extended Kalman Filter (EKF), Unscented Kalman filter. ...

State estimation theory is one of the best mathematical approaches to analyze variants in the states of the system or process. The state of the system is defined by a set of variables that provide a complete representation of the internal condition at any given instant of time. Filtering of Random processes is referred to as Estimation, and is a well-defined statistical technique. There are two...

The Extended Kalman Filter (EKF) can be used for parameter as well as for state estimation. However, when used for parameter estimation, EKF is sensitive to modeling uncertainties and prone to instability. In this paper, the Variable Structure filtering concept is introduced and used for modifying the EKF into a robust form. The proposed method is model-based and has a guaranteed stability give...

Abstract: Kalman filter is a well known adaptive filtering Algorithm, widely used for target tracking applications. When the system model and measurements are non linear, variation of Kalman filter like extended Kalman filter (EKF) is used. For obtaining reliable estimate of the target state, filter has to be tuned before the operation (off line).Tuning an EKF is the process of estimation of th...

This paper presents a comparison of different fitters namely: Extended Kalman Filter (EKF), Particle Filter (PF) and a proposed Enhanced Particle / Kalman Filter (EPKF) used in robot localization. These filters are implemented in matlab environment and their performances are evaluated in terms of computational time and error from ground truth and the results are reported. The considered robot l...

It is known that the Kalman filter estimation performance heavily depends on the statistical parameters to both the dynamic and observation models, especially the covariance matrix Q. This paper presents a fast genetic algorithm (GA) to adapt extended Kalman filter (EKF) for real time tracking. In the proposed method, the covariance matrix Q can be real-time adjusted by GA to meet some specific...