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Kalman Filter (Equations)

Kalman Filter (Equations)

Statistical Glossary

Kalman Filter (Equations):

The basic mathematics behind the idea of Kalman filter may be described as follows - Consider, for example, a Markov chain - i.e. a random series with Markov property - described by the following equation:

(1)

where

  • Math image - is the value of the vector-values Markov chain Math image at the moment Math image of discrete time Math image ;

  • Math image is a known matrix which describes regular causal link between the current state Math image and the next state Math image ;

  • Math image is a zero-mean random noise with known covariance matrix.

The Kalman filter for time series Math image specified by equation (1) may be described by the following recursive expression:

(2)

where

  • Math image is the output of the Kalman filter, which is normally used as a predictor for Math image ;

  • Math image is the "Kalman gain".

The expression (2) is only one of several mathematically- equivalent forms. It shows that the Kalman filter output Math image is a sum of two terms. The first term ( Math image ) is a dynamic prognosis based on the known transition matrix Math image of the Markov chain (1) ; the second term is a linear function of the prediction error ( Math image ) from the previous step.

The theory of Kalman filtering specifies expressions for calculation of the optimal matrix Math image in equation (2) from the known covariance matrix Math image of the random vector Math image in the model (1) .

Besides the simplest case described by (1-2) , the classical theory of Kalman filters covers more complex settings, e.g. when the state vector Math image is not observable - i.e. only a linear function Math image of Math image is known

where

  • Math image is a known matrix;

  • Math image is the observation noise with known covariance matrix.

There are also generalizations of the Kalman filter theory for continuous time Math image , expressed in terms of differential equations (not difference equations, like above); and there are extensions to non-linear filtering.

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