An Improved Tobit Kalman Filter with Adaptive Censoring Limits

Adaptive fading Kalman filter with an application

Abs t rae t -A new adaptive state estimation algorithm, namely adaptive fading Kalman filter (AFKF), is proposed to solve the divergence problem of Kalman filter. A criterion function is constructed to measure the optimality of Kalman filter. The forgetting factor in AFKF is adaptively adjusted by minimizing the defined criterion function using measured outputs. The algorithm remains convergent...

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...

Robust Fingertip Tracking with Improved Kalman Filter

This paper presents a novel approach to reliably tracking multiple fingertips simultaneously using a single optical camera. The proposed technique uses the skin color model to extract the hand region and identifies fingertips via curvature detection. It can remove different types of interfering points through the cross product of vectors and the distance transform. Finally, an improved Kalman f...

Extended Kalman Filter Based Fuzzy Adaptive Filter

Fuzzy Adaptive Extended Kalman Filter

Kalman filtering is a method for estimating state variables of a dynamic systems recursively from noise-contaminated measurements. For systems with nonlinear dynamics, a natural extension of the Linear Kalman Filter (LKF), called Extended Kalman filter (EKF) is used. The Kalman filter represents one of the most popular estimation techniques for integrating signals from navigation systems, like ...