org.apache.commons.math3.filter Class KalmanFilter

```java.lang.Object
org.apache.commons.math3.filter.KalmanFilter
```

`public class KalmanFilterextends Object`

Implementation of a Kalman filter to estimate the state xk of a discrete-time controlled process that is governed by the linear stochastic difference equation:

``` xk = Axk-1 + Buk-1 + wk-1
```
with a measurement xk that is
``` zk = Hxk + vk.
```

The random variables wk and vk represent the process and measurement noise and are assumed to be independent of each other and distributed with normal probability (white noise).

The Kalman filter cycle involves the following steps:

1. predict: project the current state estimate ahead in time
2. correct: adjust the projected estimate by an actual measurement

The Kalman filter is initialized with a `ProcessModel` and a `MeasurementModel`, which contain the corresponding transformation and noise covariance matrices. The parameter names used in the respective models correspond to the following names commonly used in the mathematical literature:

• A - state transition matrix
• B - control input matrix
• H - measurement matrix
• Q - process noise covariance matrix
• R - measurement noise covariance matrix
• P - error covariance matrix

Since:
3.0
Version:
\$Id: KalmanFilter.java 1416643 2012-12-03 19:37:14Z tn \$
Kalman filter resources, An introduction to the Kalman filter by Greg Welch and Gary Bishop, Kalman filter example by Dan Simon, `ProcessModel`, `MeasurementModel`

Constructor Summary
```KalmanFilter(ProcessModel process, MeasurementModel measurement)```
Creates a new Kalman filter with the given process and measurement models.

Method Summary
` void` `correct(double[] z)`
Correct the current state estimate with an actual measurement.
` void` `correct(RealVector z)`
Correct the current state estimate with an actual measurement.
` double[][]` `getErrorCovariance()`
Returns the current error covariance matrix.
` RealMatrix` `getErrorCovarianceMatrix()`
Returns a copy of the current error covariance matrix.
` int` `getMeasurementDimension()`
Returns the dimension of the measurement vector.
` int` `getStateDimension()`
Returns the dimension of the state estimation vector.
` double[]` `getStateEstimation()`
Returns the current state estimation vector.
` RealVector` `getStateEstimationVector()`
Returns a copy of the current state estimation vector.
` void` `predict()`
Predict the internal state estimation one time step ahead.
` void` `predict(double[] u)`
Predict the internal state estimation one time step ahead.
` void` `predict(RealVector u)`
Predict the internal state estimation one time step ahead.

Methods inherited from class java.lang.Object
`clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`

Constructor Detail

KalmanFilter

```public KalmanFilter(ProcessModel process,
MeasurementModel measurement)
throws NullArgumentException,
NonSquareMatrixException,
DimensionMismatchException,
MatrixDimensionMismatchException```
Creates a new Kalman filter with the given process and measurement models.

Parameters:
`process` - the model defining the underlying process dynamics
`measurement` - the model defining the given measurement characteristics
Throws:
`NullArgumentException` - if any of the given inputs is null (except for the control matrix)
`NonSquareMatrixException` - if the transition matrix is non square
`DimensionMismatchException` - if the column dimension of the transition matrix does not match the dimension of the initial state estimation vector
`MatrixDimensionMismatchException` - if the matrix dimensions do not fit together
Method Detail

getStateDimension

`public int getStateDimension()`
Returns the dimension of the state estimation vector.

Returns:
the state dimension

getMeasurementDimension

`public int getMeasurementDimension()`
Returns the dimension of the measurement vector.

Returns:
the measurement vector dimension

getStateEstimation

`public double[] getStateEstimation()`
Returns the current state estimation vector.

Returns:
the state estimation vector

getStateEstimationVector

`public RealVector getStateEstimationVector()`
Returns a copy of the current state estimation vector.

Returns:
the state estimation vector

getErrorCovariance

`public double[][] getErrorCovariance()`
Returns the current error covariance matrix.

Returns:
the error covariance matrix

getErrorCovarianceMatrix

`public RealMatrix getErrorCovarianceMatrix()`
Returns a copy of the current error covariance matrix.

Returns:
the error covariance matrix

predict

`public void predict()`
Predict the internal state estimation one time step ahead.

predict

```public void predict(double[] u)
throws DimensionMismatchException```
Predict the internal state estimation one time step ahead.

Parameters:
`u` - the control vector
Throws:
`DimensionMismatchException` - if the dimension of the control vector does not fit

predict

```public void predict(RealVector u)
throws DimensionMismatchException```
Predict the internal state estimation one time step ahead.

Parameters:
`u` - the control vector
Throws:
`DimensionMismatchException` - if the dimension of the control vector does not match

correct

```public void correct(double[] z)
throws NullArgumentException,
DimensionMismatchException,
SingularMatrixException```
Correct the current state estimate with an actual measurement.

Parameters:
`z` - the measurement vector
Throws:
`NullArgumentException` - if the measurement vector is `null`
`DimensionMismatchException` - if the dimension of the measurement vector does not fit
`SingularMatrixException` - if the covariance matrix could not be inverted

correct

```public void correct(RealVector z)
throws NullArgumentException,
DimensionMismatchException,
SingularMatrixException```
Correct the current state estimate with an actual measurement.

Parameters:
`z` - the measurement vector
Throws:
`NullArgumentException` - if the measurement vector is `null`
`DimensionMismatchException` - if the dimension of the measurement vector does not fit
`SingularMatrixException` - if the covariance matrix could not be inverted