unscented Kalman filter (UKF)
Autonomous Excavation of Rocks Using a Gaussian Process Model and Unscented Kalman Filter
... and Unscented Kalman Filter Filippos ...An Unscented Kalman Filter (UKF) is then integrated with the Gaussian process model for predicting the rock movements and estimating ...
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A Brief Tutorial On Recursive Estimation With Examples From Intelligent Vehicle Applications (Part III): Handling Nonlinear Estimation Problems And The Unscented Kalman Filter
... the unscented Kalman filter (UKF), for its advantage over the extended Kalman filter (EKF) in handling nonlinear estimation ...the unscented Kalman ...
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Characterization of Spatio-Temporal Cardiac Action Potential Variability at Baseline and under beta-Adrenergic Stimulation by Combined Unscented Kalman Filter and Double Greedy Dimension Reduction
... the Unscented Kalman Filter (UKF) has been proposed to estimate parameters and state variables of an underlying human ventricular action potential (AP) model for any given input voltage ...
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A Brief Tutorial On Recursive Estimation With Examples From Intelligent Vehicle Applications (Part III): Handling Nonlinear Estimation Problems And The Unscented Kalman Filter
... inconsistency. In order to handle inconsistent statistics that harass the EKF in nonlinear estimation, Julier and Uhlmann proposed in [4] [8] a useful variant of the KF, i.e. the unscented Kalman ...
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A maximum likelihood-based unscented Kalman filter for multipath mitigation in a multi-correlator based GNSS receiver
... the signal parameters. On the contrary, some alternative approaches exploit time propagation models associated with the unknown pa- rameters of LOS and MP signals. The objective is then to estimate recursively the ...
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A maximum likelihood-based unscented Kalman filter for multipath mitigation in a multi-correlator based GNSS receiver
... the signal parameters. On the contrary, some alternative approaches exploit time propagation models associated with the unknown pa- rameters of LOS and MP signals. The objective is then to estimate recursively the ...
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Aircraft attitude estimation based on central difference Kalman filter
... appropriate filter is utilized to estimate the ...extended Kalman filter (EKF) is probably the most widely used at ...issue, unscented Kalman filter (UKF) was proposed by Julier ...
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Personalization of Cardiac Electrophysiology Model using the Unscented Kalman Filtering
... Department of Cardiovascular Imaging, King’s College, London, United Kingdom 4 Department of Cardiology, Guy’s and St. Thomas’ NHS, London, United Kingdom Abstract. Cardiac electrophysiology mapping techniques now allow ...
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Multi-rate unscented Kalman filtering for pose and curvature estimation in 3D ultrasound-guided needle steering
... rate unscented Kalman filter is proposed for the first time in needle steering to fuse asynchronous data coming from 3D B-mode ultrasound images, robot sensors and pre-operative elastography ...
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Implementation of Kalman Filter with Python Language
... 1: Kalman Filter Applied to ToA Based Localization ...the Kalman Filter ...Extended Kalman Filter, Unscented Kalman Filter and Particle ...
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Three examples of the stability properties of the invariant extended Kalman filter
... the filter should not diverge for sufficiently small errors and ...following filter accomodates discrete-time observations, which can be relevant in prac- tice (for instance a star tracking system in a ...
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Improving Inertial Velocity Estimation Through Magnetic Field Gradient-based Extended Kalman Filter
... nonlinearity issues, is proposed to estimate the velocity and eventually attitude of a rigid body in a magnetically dis- turbed environment, from a 3-axis magnetometers array, 3-axis gyroscope and 3-axis accelerometer. ...
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Experimental Implementation of an Invariant Extended Kalman Filter-based Scan Matching SLAM
... I. I NTRODUCTION Simultaneous Localization and Mapping or SLAM is an active area of research in robotics due to its use in emerging applications such as autonomous driving and piloting, search- and-rescue missions and ...
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Covariance Correction For Estimating Groundwater Level Using Deterministic Ensemble Kalman Filter
... P ) in the filter scheme, it is easy to implement schur localization for it. Problems Concerning Small Ensembles Using small ensembles for calculating error covariance matrix can introduce sampling errors as false ...
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Kalman filter technique for multisite modelling and streamflow prediction in Algeria
... k in Equation 3). This figure gives a value of 10*4 to the initial covariance matrix trace (major diagonal elements sum). Such a value is expected to decrease continuously and asymptotically to a stable positive value ...
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Variations on the Kalman filter for enhanced performance monitoring of gas turbine engines
... The Kalman filter is the mmse recursive estimator for stochastic linear models with Gaus- sian random ...particle filter [see ...the Kalman filter produce quite satisfactory results as ...
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An Ensemble Kalman Filter for severe dust storm data assimilation over China
... Received: 17 September 2007 – Published in Atmos. Chem. Phys. Discuss.: 3 December 2007 Revised: 8 May 2008 – Accepted: 14 May 2008 – Published: 17 June 2008 Abstract. An Ensemble Kalman Filter (EnKF) data ...
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Anomaly detection with the Switching Kalman Filter for structural health monitoring
... 0 and covariance Σ 0 for the hidden states. The log-likelihood function is usually non-convex; poor guesses for either initial parameter values or hidden state initial values are prone to lead to a local maximum. In the ...
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Validation and parameter sensitivity test for reconstructing swell field based on Ensemble Kalman filter
... * Correspondence: gechen@ouc.edu.cn; Tel.: +86-532-6678-1265 Academic Editor: Changzhi Li Received: 21 May 2016; Accepted: 11 November 2016; Published: 25 November 2016 Abstract: The swell propagation model built on ...
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History-matching of petroleum reservoir models by the ensemble Kalman filter and parameterization methods
... The problem of non-Gaussianity can be dealt with a "parameterization" method to map non- Gaussian variables to a Gaussian one, that is the one updated through the EnKF methodology. Then, a backward transform ...
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