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An Introduction to the Kalman Filter
- UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL
, 1995
"... In 1960, R.E. Kalman published his famous paper describing a recursive solution to the discrete-data linear filtering problem. Since that time, due in large part to advances in digital computing, the Kalman filter has been the subject of extensive research and application, particularly in the area o ..."
Abstract
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Cited by 1146 (13 self)
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In 1960, R.E. Kalman published his famous paper describing a recursive solution to the discrete-data linear filtering problem. Since that time, due in large part to advances in digital computing, the Kalman filter has been the subject of extensive research and application, particularly in the area
A New Extension of the Kalman Filter to Nonlinear Systems
, 1997
"... The Kalman filter(KF) is one of the most widely used methods for tracking and estimation due to its simplicity, optimality, tractability and robustness. However, the application of the KF to nonlinear systems can be difficult. The most common approach is to use the Extended Kalman Filter (EKF) which ..."
Abstract
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Cited by 778 (6 self)
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The Kalman filter(KF) is one of the most widely used methods for tracking and estimation due to its simplicity, optimality, tractability and robustness. However, the application of the KF to nonlinear systems can be difficult. The most common approach is to use the Extended Kalman Filter (EKF
Kalman filtering with intermittent observations
- IEEE TRANSACTIONS ON AUTOMATIC CONTROL
, 2004
"... Motivated by navigation and tracking applications within sensor networks, we consider the problem of performing Kalman filtering with intermittent observations. When data travel along unreliable communication channels in a large, wireless, multihop sensor network, the effect of communication delays ..."
Abstract
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Cited by 295 (41 self)
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Motivated by navigation and tracking applications within sensor networks, we consider the problem of performing Kalman filtering with intermittent observations. When data travel along unreliable communication channels in a large, wireless, multihop sensor network, the effect of communication
The ensemble Kalman Filter: Theoretical formulation and practical implementation.
- Ocean Dynamics,
, 2003
"... Abstract The purpose of this paper is to provide a comprehensive presentation and interpretation of the Ensemble Kalman Filter (EnKF) and its numerical implementation. The EnKF has a large user group and numerous publications have discussed applications and theoretical aspects of it. This paper rev ..."
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Cited by 496 (5 self)
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Abstract The purpose of this paper is to provide a comprehensive presentation and interpretation of the Ensemble Kalman Filter (EnKF) and its numerical implementation. The EnKF has a large user group and numerous publications have discussed applications and theoretical aspects of it. This paper
Data Assimilation Using an Ensemble Kalman Filter Technique
, 1998
"... The possibility of performing data assimilation using the flow-dependent statistics calculated from an ensemble of short-range forecasts (a technique referred to as ensemble Kalman filtering) is examined in an idealized environment. Using a three-level, quasigeostrophic, T21 model and simulated ob ..."
Abstract
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Cited by 423 (5 self)
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The possibility of performing data assimilation using the flow-dependent statistics calculated from an ensemble of short-range forecasts (a technique referred to as ensemble Kalman filtering) is examined in an idealized environment. Using a three-level, quasigeostrophic, T21 model and simulated
Mixture Kalman filters
, 2000
"... In treating dynamic systems,sequential Monte Carlo methods use discrete samples to represent a complicated probability distribution and use rejection sampling, importance sampling and weighted resampling to complete the on-line `filtering' task. We propose a special sequential Monte Carlo metho ..."
Abstract
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Cited by 224 (8 self)
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method,the mixture Kalman filter, which uses a random mixture of the Gaussian distributions to approximate a target distribution. It is designed for on-line estimation and prediction of conditional and partial conditional dynamic linear models,which are themselves a class of widely used non
Kalman Filter
, 2001
"... Forecasts are rarely perfect; instead they show what is likely to happen “on average”. So it is a good practice to complement forecasts with measures of the forecast uncertainty. The most common measure of uncertainty is the variance. Such measures are particularly useful for decision making. For ex ..."
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. For example, when determining the amount of stock to keep in a warehouse, it is necessary to be able to meet above normal levels of demand, not just the average demand. The amount stock in the warehouse should be based on a measure of uncertainty such as the forecast variance. The Kalman Filter
KALMAN FILTERING
"... Do not ask permission to understand. Do not wait for the word of authority. Size reason in your own hand. With your own teeth savor the fruit. 1 1 Preface of [10] ii This project, supervised by Dr. K.A. Lindsay, contributes one paper to the 4H Honours course. The project concerns the use of the Kalm ..."
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of the Kalman Filter as an applied tool and includes some derivations of background theory. Some knowledge of statistics and the theory of deterministic linear differential equations is assumed but all stochastic work is developed in detail. The theory is applied to the Vasicek model of interest rates. All
The Kalman Filter
, 2002
"... The Kalman Filter developed in the early sixties by R.E. Kalman [7, 8] is a recursive state estimator for partially observed non-stationary stochastic processes. It gives an optimal estimate in the least squares sense of the actual value of a state vector from noisy observations. 1 Recursive State E ..."
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The Kalman Filter developed in the early sixties by R.E. Kalman [7, 8] is a recursive state estimator for partially observed non-stationary stochastic processes. It gives an optimal estimate in the least squares sense of the actual value of a state vector from noisy observations. 1 Recursive State
Results 1 - 10
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8,119
