Gaussian filters for nonlinear filtering problems

By Kazufumi Ito, Kaiqi Xiong. IEEE Transactions on Automatic Control, 2000

1,586 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/bc3b29196be501bf570f856d048a290c4e3de66a

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Citations in the corpus

Unscented Kalman filtering for nonlinear structural dynamics (2007, methodology): “To ensure accuracy of the results, in the analyses we have always adopted ∆t < ∆t?, ∆t? being the threshold at which a further reduction of the time step size do not modify the computed system response.”
Bayesian Filtering and Smoothing (2013, methodology): “4 Gauss–Hermite Kalman filter The additive form multi-dimensional Gauss–Hermite cubature based Gauss–Hermite (Kalman) filter (GHKF) (Ito and Xiong, 2000) which is also called the quadrature Kalman filter (QKF) (Arasaratnam et al.”
A Numerical-Integration Perspective on Gaussian Filters (2006, methodology): “Applying the above product Gauss–Hermite rule to calculate the five integrals in (3) gives birth to the GHF [7], [23], [24].”
An Overview of Existing Methods and Recent Advances in Sequential Monte Carlo (2007, methodology): “Two representative algorithms in this class are the unscented Kalman filter (UKF) [11], [12] and the Gaussian quadrature Kalman filter (QKF) [13].”
Kalman filters for non-linear systems: a comparison of performance (2004, neutral): “, higher order filters, [7], [8], [9] and Gaussian sum filters, [16], [17].”
Discrete-Time Nonlinear Filtering Algorithms Using Gauss–Hermite Quadrature (2007, methodology): “Recently, Ito and Xiong introduced another suboptimal, nonlinear filter called the quadrature Kalman filter (QKF) [ 27 ].”
Entropy-Based Approach for Uncertainty Propagation of Nonlinear Dynamical Systems (2013, background): “More recently, a new class of nonlinear inference tools, called sigma-point Kalman filters has emerged, chief among them the unscented Kalman filter (UKF) [7,8] and the central difference Kalman filter [9,10].”
A Unifying Framework for Gaussian Process Pseudo-Point Approximations using Power Expectation Propagation (2016, background): “In fact, as the focus was online inference, Assumed Density Filtering (Kushner and Budhiraja, 2000; Ito and Xiong, 2000) was employed rather than full-blown EP.”
Applied Stochastic Differential Equations (2019, positive): “(2015b) and in the theses of Jeisman (2005) and Mbalawata (2014) as well as in the books of Rao (1999) and Iacus (2008).”
CUDA-Based Parallelization of Power Iteration Clustering for Large Datasets (2017, methodology): “In order to compare cluster quality outcomes, the images were pre-processed with two different filters, Gaussian [18] and Median [19].”
Cubature Kalman Filters (2009, methodology): “For example, we may assume it to be Gaussian; the nonlinear filters, namely, the extended Kalman filter (EKF) [7], the central-difference Kalman filter (CDKF) [8], [9], the unscented Kalman filter (UKF) [10], and the quadrature Kalman filter (QKF) [11], [12], fall under this first category.”