New developments in state estimation for nonlinear systems

By M. Nørgaard, Niels Kjølstad Poulsen, Ole Ravn. at - Automatisierungstechnik, 2000

649 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Artificial Intelligence, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/c592d98e45d7bd272a9af6b0dd26d67cdd3d0b7f

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In Aerospace Engineering

In Artificial Intelligence

In Statistical and Nonlinear Physics

Citations in the corpus

Sigma-point kalman filters for probabilistic inference in dynamic state-space models (2004, positive): “For Gaussian priors, its optimal value is [ 31 ].”
Nonlinear Regression Huber–Kalman Filtering and Fixed-Interval Smoothing (2015, neutral): “Five filters are implemented for comparison: EKF, Huber-EKF, IEKF, and Huber-IEKF, and the second-order divided difference filter (DD2) [36].”
Improved estimation of state of stochastic systems via invariant embedding technique (2008, neutral): “Since Kalman’s noteworthy paper [1], the problem of state estimation in linear and nonlinear systems has been treated extensively and various aspects of the problem have been analyzed [2-8].”
Kalman filters for non-linear systems: a comparison of performance (2004, positive): “This paper focuses on the following variants of the Kalman Filter: the Extended Kalman Filter (EKF, [6], [7], [8], [9]), the Iterated Extended Kalman Filter (IEKF, [6], [7], [8], [9]), the Central Difference Filter (CDF, [10]), the first order Divided Difference Filter (DD1, [11], [12]) and the Unsc”
Nonlinear Dynamical System Identification from Uncertain and Indirect Measurements (2004, neutral): “The ideas of unscented Kalman filtering go back to the more general idea of stochastic approximation [Robbins & Munroe, 1951; Phaneuf, 1968; Sunahara, 1969; Mahalanabis & Farooq, 1971] and are put forward in [Norgaard et al., 2000].”
Discrete-Time Nonlinear Filtering Algorithms Using Gauss–Hermite Quadrature (2007, positive): “Another Gaussian filter, known as the divided difference filter (DDF) was introduced in [ 44 ] using Stirling’s interpolations formula.”
Reduced sigma point filters for the propagation of means and covariances through nonlinear transformations (2002, positive): “Very recently, methods analogous to the UT approach have been derived from a central difierence perspective [ 13 ].”
Dynamic modeling of neuronal responses in fMRI using cubature Kalman filtering (2011, positive): “As with the UKF, DDF 255 uses a deterministic sampling approach to propagate Gaussian statistics through the nonlinear 256 function; e) the Gaussian sum filters (GSF) approximates both the predicted and posterior 257 densities as sum of Gaussian densities, where the mean and covariance of each Gauss”
A Numerical-Integration Perspective on Gaussian Filters (2006, neutral): “Unsurprisingly, the overall reported results in the literature [1]‐[3], [7], [ 10 ], [19], [21] accord to the list.”