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Unscented filtering and nonlinear estimation
TLDR
The motivation, development, use, and implications of the UT are reviewed, which show it to be more accurate, easier to implement, and uses the same order of calculations as linearization.
New extension of the Kalman filter to nonlinear systems
TLDR
It is argued that the ease of implementation and more accurate estimation features of the new filter recommend its use over the EKF in virtually all applications.
A new method for the nonlinear transformation of means and covariances in filters and estimators
TLDR
A new approach for generalizing the Kalman filter to nonlinear systems is described, which yields a filter that is more accurate than an extendedKalman filter (EKF) and easier to implement than an EKF or a Gauss second-order filter.
Recent Advances in Augmented Reality
TLDR
This work refers one to the original survey for descriptions of potential applications, summaries of AR system characteristics, and an introduction to the crucial problem of registration, including sources of registration error and error-reduction strategies.
A General Method for Approximating Nonlinear Transformations of Probability Distributions
TLDR
A new approach for generalised nonlinear ltering is described, which is more accurate, more stable, and far easier to implement than an extended Kalman lter.
Which Is Better, an Ensemble of Positive–Negative Pairs or a Centered Spherical Simplex Ensemble?
Abstract New methods to center the initial ensemble perturbations on the analysis are introduced and compared with the commonly used centering method of positive–negative paired perturbations. In the
Sigma-Point Kalman Filters for Nonlinear Estimation and Sensor-Fusion: Applications to Integrated Navigation
TLDR
A probabilistic framework, called Sigma-point Kalman Filters (SPKF) was applied to the problem domain addressed by the extended Kalman Filter, and the SPKF-based sensor latency compensation technique is used to demonstrate the lagged GPS measurements.
Reduced sigma point filters for the propagation of means and covariances through nonlinear transformations
TLDR
Methods for minimizing the number of sigma points for real-time control, estimation, and filtering applications are examined and results are demonstrated in a 3D localization example.
Corrections to "Unscented Filtering and Nonlinear Estimation"
TLDR
It is observed that EKF yields an inconsistent estimate, while the unscented Kalman filter yields a consistent estimate.
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