## Types in MathNet.Filtering.Kalman

Type IKalmanFilter

Namespace MathNet.Filtering.Kalman

An interface to describe a Kalman Filter. A Kalman filter is a recursive solution to the general dynamic estimation problem for the important special case of linear system models and Gaussian noise.

The Kalman Filter uses a predictor-corrector structure, in which if a measurement of the system is available at time , we first call the Predict function, to estimate the state of the system at time. We then call the Update function to correct the estimate of state, based on the noisy measurement.

### Public Methods

#### voidPredict(Matrix<double> F)

Performs a prediction of the next state of the system.
##### Parameters
###### `Matrix<double>` F

The state transition matrix.

#### voidPredict(Matrix<double> F, Matrix<double> G, Matrix<double> Q)

Perform a prediction of the next state of the system.
##### Parameters
###### `Matrix<double>` F

The state transition matrix.

###### `Matrix<double>` G

The linear equations to describe the effect of the noise on the system.

###### `Matrix<double>` Q

The covariance of the noise acting on the system.

#### voidUpdate(Matrix<double> z, Matrix<double> H, Matrix<double> R)

Updates the state estimate and covariance of the system based on the given measurement.
##### Parameters
###### `Matrix<double>` z

The measurements of the system.

###### `Matrix<double>` H

Linear equations to describe relationship between measurements and state variables.

###### `Matrix<double>` R

The covariance matrix of the measurements.

### Public Properties

#### Matrix<double>Cov get;

The covariance of the current state estimate.

#### Matrix<double>State get;

The current best estimate of the state of the system.