Results 131 to 140 of about 66,114 (285)
A fully digital model for Kalman filters
, 2010 The Kalman filter is a mathematical method, whose purpose is to process noisy measurements in order to obtain an estimate of some relevant parameters of a system.
Lo Presti, Letizia +2 more
core
A structurally localized ensemble Kalman filtering approach
Quarterly Journal of the Royal Meteorological Society, EarlyView.We derive an inherently localized ensemble Kalman filtering (EnKF) approach, avoiding the need for any auxiliary localization technique. The idea is to first use the variational Bayesian optimization to approximate the (continuous) state analysis probability density function (pdf) by a product of independent marginal pdfs corresponding to small ...
Boujemaa Ait‐El‐Fquih +1 more
wiley +1 more source
KALMAN FILTERS AND ARMA MODELS
Ratio Mathematica, 2003 The Kalman filter is the celebrated algorithm giving a recursive solution of the prediction problem for time series. After a quite general formulation of the prediction problem, the contributions of its solution by the great mathematicians Kolmogorov and
Aniello Fedullo
doaj
Location of leaks in pipelines using parameter identification tools [PDF]
, 2017 This work proposes an approach to locate leaks by identifying the parameters of finite models associated with these fault events. The identification problem is attacked by using well-known identification methods such as the Prediction Error Method and ...
Torres, L.
core
Quarterly Journal of the Royal Meteorological Society, EarlyView.We document for the first time how the assimilation of CS2SMOS observations improves the model representation of Arctic sea‐ice thickness (SIT) and its variability: biases are reduced (top row), while excessive variability in the Beaufort Sea and lack of variability in the ice pack are both corrected (bottom row).
Jiping Xie +3 more
wiley +1 more source
The diffuse ensemble filter [PDF]
Nonlinear Processes in Geophysics, 2009 A new class of ensemble filters, called the Diffuse Ensemble Filter (DEnF), is proposed in this paper. The DEnF assumes that the forecast errors orthogonal to the first guess ensemble are uncorrelated with the latter ensemble and have infinite variance ...
X. Yang, T. DelSole
doaj
Measurement Noise Covariance-Adapting Kalman Filters for Varying Sensor Noise Situations. [PDF]
Sensors (Basel), 2021 Chhabra A, Venepally JR, Kim D.
europepmc +1 more source
Initial State Privacy of Nonlinear Systems on Riemannian Manifolds
International Journal of Robust and Nonlinear Control, EarlyView.ABSTRACT In this paper, we investigate initial state privacy protection for discrete‐time nonlinear closed systems. By capturing Riemannian geometric structures inherent in such privacy challenges, we refine the concept of differential privacy through the introduction of an initial state adjacency set based on Riemannian distances.
Le Liu, Yu Kawano, Antai Xie, Ming Cao
wiley +1 more source
Regularized Optimal Transport Based on an Adaptive Adjustment Method for Selecting the Scaling Parameters of Unscented Kalman Filters. [PDF]
Sensors (Basel), 2022 Kang CH, Kim SY.
europepmc +1 more source
Vectorization of linear discrete filtering algorithms [PDF]
Linear filters, including the conventional Kalman filter and versions of square root filters devised by Potter and Carlson, are studied for potential application on streaming computers.
Schiess, J. R.
core +1 more source