A Robust Adaptive Unscented Kalman Filter for Floating Doppler Wind-LiDAR Motion Correction

Doppler and bearing tracking using fuzzy adaptive unscented Kalman filter

The topic of Doppler and Bearing Tracking (DBT) problem is to achieve a target trajectory using the Doppler and Bearing measurements. The difficulty of DBT problem comes from the nonlinearity terms exposed in the measurement equations. Several techniques were studied to deal with this topic, such as the unscented Kalman filter. Nevertheless, the performance of the filter depends directly on the...

A Hybrid Adaptive Unscented Kalman Filter Algorithm

In order to overcome the limitation of the traditional adaptive Unscented Kalman Filtering (UKF) algorithm in noise covariance estimation for statement and measurement, we propose a hybrid adaptive UKF algorithm based on combining Maximum a posteriori (MAP) criterion and Maximum likelihood (ML) criterion, in this paper. First, to prevent the actual noise covariance deviating from the true value...

Fuzzy Adaptive Variational Bayesian Unscented Kalman Filter

We consider the problem of nonlinear filtering under the circumstance of unknown covariance statistic of the measurement noise. A novel adaptive unscented Kalman filter (UKF) integrating variational Bayesian methods and fuzzy logic techniques is proposed in this paper. It is called fuzzy adaptive variational Bayesian UKF (FAVBUKF). Firstly, the sufficient statistics of the measurement noise var...

Robust Adaptive Unscented Particle Filter

Rotated Unscented Kalman Filter for Two State Nonlinear Systems

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...