This paper presents sensor data fusion using Unscented Kalman Filter (UKF) to implement high performance vestibulo-ocular reflex (VOR) based vision tracking system for mobile robots. Information from various sensors is required to be integrated using an efficient sensor fusion algorithm to achieve a continuous and robust vision tracking system. We use data from low cost accelerometer, gyroscope...

BACKGROUND Diffusion imaging tractography is increasingly used to trace critical fiber tracts in brain tumor patients to reduce the risk of post-operative neurological deficit. However, the effects of peritumoral edema pose a challenge to conventional tractography using the standard diffusion tensor model. The aim of this study was to present a novel technique using a two-tensor unscented Kalma...

Target tracking using bistatic bearings-only measurements has obtained distinct interest recently. It is a nonlinear problem that traditional Kalman filter (KF) can not be applied directly. In this paper, the triangular ranging formula has been derived first for bistatic bearings-only tracking. The ranging error is then proved to be Gaussian noises, which enable the traditional KF applicable. T...

To ensure safe and reliable battery operations, an accurate battery state of charge (SOC) estimation is critical for the battery systems used in electric vehicles and hybrid electric vehicles because of the arduous operation conditions. This paper presents a SOC estimator designed based on the unscented Kalman filter (UKF), which is very popular in the state estimation in non-linear systems. Th...

Accurate position and velocity estimation of a re-entry vehicle is essential for realizing its deviation from the desired descent trajectory and providing necessary guidance command in real-time. Generally the Extended Kalman Filter (EKF) is utilized for position and velocity estimation of a space vehicle. However, in the EKF the error covariance is predicted by linearizing the non-linear dynam...

This article presents a new nonlinear joint (state and parameter) estimation algorithm based on fusion of Kalman filter and randomized unscented Kalman filter (UKF), called Kalman randomized joint UKF (KR-JUKF). It is assumed that the measurement equation is linear. The KRJUKF is suitable for time varying and severe nonlinear dynamics and does not have any systematic error. Finally, joint-EKF, ...

The Extended Kalman Filter (EKF) has long been the de-facto standard for nonlinear state space estimation [11], primarily due to its simplicity, robustness and suitability for realtime implementations. However, an alternative approach has emerged over the last few years, namely the unscented Kalman filter (UKF). This filter claims both higher accuracy and robustness for nonlinear models. Severa...

Ellipticine is an antineoplastic agent, whose mode of action is based mainly on DNA intercalation, inhibition of topoisomerase II and formation of covalent DNA adducts mediated by cytochromes P450 and peroxidases. Here, the molecular mechanism of DNA-mediated ellipticine action in human neuroblastoma IMR-32, UKF-NB-3 and UKF-NB-4 cancer cell lines was investigated. Treatment of neuroblastoma ce...

this paper extends the sequential learning algorithm strategy of two different types of adaptive radial basis function-based (rbf) neural networks, i.e. growing and pruning radial basis function (gap-rbf) and minimal resource allocation network (mran) to cater for on-line identification of non-linear systems. the original sequential learning algorithm is based on the repetitive utilization of s...

The unscented Kalman filter (UKF) is adopted in the interacting multiple model (IMM) framework to deal with the system nonlinearity in navigation applications. The adaptive tuning system (ATS) is employed for assisting the unscented Kalman filter in the IMM framework, resulting in an interacting multiple model adaptive unscented Kalman filter (IMM-AUKF). Two models, a standard UKF and an adapti...