Topic
Invariant extended Kalman filter
About: Invariant extended Kalman filter is a research topic. Over the lifetime, 7079 publications have been published within this topic receiving 187702 citations.
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Papers
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TL;DR: The positive result of this study is that the U-D covariance factorization algorithm has excellent numerical properties and is computationally efficient, having CPU costs that differ negligibly from the conventional Kalman costs.
139 citations
TL;DR: The formulation of a Kalman filter system for assimilating limb-sounding observations of stratospheric chemical constituents into a tracer transport model is described, based on a two-dimensional isentropic approximation.
Abstract: The first part of this two-part article describes the formulation of a Kalman filter system for assimilating limb-sounding observations of stratospheric chemical constituents into a tracer transport model. The system is based on a two-dimensional isentropic approximation, permitting a full Kalman filter implementation and a thorough study of its behavior in a real-data environment. Datasets from two instruments on the Upper Atmosphere Research Satellite with very different viewing geometries are used in the assimilation experiments. A robust chi-squared diagnostic, which compares statistics of the observed-minus-forecast residuals with those calculated by the filter algorithm, is used to help formulate the statistical inputs to the filter, as well as to tune covariance parameters and to validate the assimilation results. Two significant departures from the standard (discrete) Kalman filter formulation were found to be important in this study. First, it was discovered that the standard Kalman filt...
139 citations
Journal Article•
TL;DR: The ensemble Kalman filter (EnKF) as discussed by the authors is a sequential Monte Carlo method that provides an alternative to the traditional KF and adjoint or four-dimensional variational (4DVAR) methods.
Abstract: he ensemble Kalman filter (EnKF) [1] is a sequential Monte Carlo method that provides an alternative to the traditional Kalman filter (KF) [2], [3] and adjoint or four-dimensional variational (4DVAR) methods [4]-[6] to better handle large state spaces and nonlinear error evolution. EnKF provides a simple conceptual formulation and ease of implementation, since.
138 citations
TL;DR: Kalman filter alternate form extended to include multiple simultaneous correlated measurements, testing with ballistic model and using square root formulation for trajectory determination as discussed by the authors was used to determine the trajectory of a vehicle.
Abstract: Kalman filter alternate form extended to include multiple simultaneous correlated measurements, testing with ballistic model and using square root formulation for trajectory determination
138 citations
TL;DR: The methodology proposed in this technical note can be used to construct nonlinear filters with improved accuracy for certain problems, and the performance of the proposed algorithm is demonstrated through a nonlinear high dimensional problem.
Abstract: This technical note concerns the deterministic sampling points construction strategy for unscented Kalman filter (UKF) and cubature Kalman filter (CKF). From the numerical-integration viewpoint, a new deterministic sampling points set is derived by orthogonal transformation on the cubature points. By embedding these points into the UKF framework, a modified nonlinear filter named transformed unscented Kalman filter (TUKF) is derived. The TUKF can address the nonlocal sampling problem inherent in CKF while maintaining the virtue of numerical stability for high dimensional problems. Moreover, the methodology proposed in this technical note can be used to construct nonlinear filters with improved accuracy for certain problems. The performance of the proposed algorithm is demonstrated through a nonlinear high dimensional problem.
138 citations