Gaussian process regression-based quaternion unscented Kalman robust filter for integrated SINS/GNSS

A Quaternion-based Unscented Kalman Filter for Orientation Tracking

This paper describes a Kalman filter for the real-time estimation of a rigid body orientation from measurements of acceleration, angular velocity and magnetic field strength. A quaternion representation of the orientation is computationally effective and avoids problems with singularities. The nonlinear relationship between estimated orientation and expected measurement prevent the usage of a c...

Tightly-coupled INS/GPS using Quaternion-based Unscented Kalman filter

PS receiver has dominated the field of positioning and navigation for decades [1]. However, its performance depends on the signal environments. It provides a continuous navigation solution only when more than four satellites are in view. In order to solve this problem, other navigation systems, e.g., inertial navigation system, are often employed to integrate with GPS for having a robust and co...

Quaternion-Based Unscented Kalman Filter for Robust Motion Tracking in Neurosurgery

A KNN Based Kalman Filter Gaussian Process Regression

The standard Gaussian process (GP) regression is often intractable when a data set is large or spatially nonstationary. In this paper, we address these challenging data properties by designing a novel K nearest neighbor based Kalman filter Gaussian process (KNN-KFGP) regression. Based on a state space model established by the KNN driven data grouping, our KNN-KFGP recursively filters out the la...

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...