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...

In this paper we investigate the use of an alternative to the extended Kalman filter (EKF), the unscented Kalman filter (UKF). First we give a broad overview of different UKF algorithms, then present an extension to the ensemble of UKF algorithms, and finally address the issue of how to add constraints using the UKF approach. The performance of the constrained approach is compared with EKF and ...

This paper is the second of a two-part series that discusses the implementation issues and test results of a robust Unscented Kalman Filter (UKF) for power system dynamic state estimation with non-Gaussian synchrophasor measurement noise. The tuning of the parameters of our Generalized MaximumLikelihood-type robust UKF (GM-UKF) is presented and discussed in a systematic way. Using simulations c...

The use of the direct filtering approach for INS/GNSS integrated navigation introduces nonlinearity into the system state equation. As the unscented Kalman filter (UKF) is a promising method for nonlinear problems, an obvious solution is to incorporate the UKF concept in the direct filtering approach to address the nonlinearity involved in INS/GNSS integrated navigation. However, the performanc...

For the general nonlinear systems, a universal weighted measurement fusion (WMF) algorithm is presented via the Taylor series expansion method. Based on the proposed fusion algorithm and the well-known Unscented Kalman Filter (UKF), the WMF-UKF is presented. It is proven that the proposed WMF-UKF asymptotically approaches to the centralized measurement fusion UKF (CMF-UKF) with the increase of ...

this paper studies the use of unscented kalman filters (ukf) to estimate nonlinear dynamics and, specifically, adaptive determination of scaling parameters in these filters. due to lack of analytic solution and use of numerical methods instead, the computational load of these filters increases drastically. in this paper, a new method is proposed based on interactive multiple models (imm) which ...

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) and Unscented Kalman filters (UKF) are used. For obtaining reliable estimate of the target state, filter has to be tuned before the operation (off line).Tuning an UKF is the p...

The Extended Kalman Filter (EKF) is a well established technique for position and velocity estimation. However, the performance of the EKF degrades considerably in highly non-linear system applications as it requires local linearisation in its prediction stage. The Unscented Kalman Filter (UKF) was developed to address the non-linearity in the system by deterministic sampling. The UKF provides ...

Combined with strong tracking filter (STF) theory, the Strong Tracking Square-Root Unscented Kalman Filter (UKF)-based satellite attitude determination algorithm is proposed in this paper. QR decomposition and Cholesyk decomposition are introduced in this paper, which improves the stability of filter. In addition, by introduced adaptive fading factor, the prediction error covariance matrix can ...

Based on presentation of the principles of the EKF and UKF for state estimation, we discuss the differences of the two approaches. Four rather different simulation cases are considered to compare the performance. A simple procedure to include state constraints in the UKF is proposed and tested. The overall impression is that the performance of the UKF is better than the EKF in terms of robustne...