Kalman filter


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Kalman filter

[′kal·mən ‚fil·tər]
(control systems)
A linear system in which the mean squared error between the desired output and the actual output is minimized when the input is a random signal generated by white noise.
References in periodicals archive ?
The model is estimated using the Kalman filter [Laubach and Williams 2003].
In [15], to design the Kalman filter for a singular system, the covariance matrix is initially considered as P(t) = E [x(t)[x.
Traffic density was used to indicate traffic congestion and was estimated using a model-based estimation scheme developed using the extended Kalman filter.
Using (46) and (47), the Kalman filter gain under RI can be written as
Extended Kalman Filter (EKF) is the filter to handle non-linear problems, by linearizing the estimation using partial derivatives of the process and measurement functions (Welch and Bishop 2006).
The optimal Kalman filter application allows the problem of state vector recovery from noisy measurements Y(t) to be solved in real time, providing an estimation of minimum mean square error of the state variables [x.
Another major problem with Kalman filter is that it can model only a single hypothesis.
The simplest version of this idea is the ensemble Kalman filter (EnKF), first proposed in [11], where the covariance matrices in the KF formulas are essentially replaced with sample statistics calculated from the ensemble.
After properly replacing the missing observations using the ARMA and Kalman filter methods, we first limited the cointegation system to measures of barter transactions and business cycle variables.
The Kalman filter may be regarded similar to the hidden Markov model, with the key difference that the hidden state variables are continuous (as opposed to being discrete in the hidden Markov model).
The modeling strategy involves using the Kalman filter with time-varying parameters and non-differenced data.
In the first case the matrix is computed from the Kalman Filter, the identification algorithm became: