KalmanFilter (Apache Commons Math 3.6.1 API)

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

```
public class KalmanFilter
extends Object
```
Implementation of a Kalman filter to estimate the state x_k of a discrete-time controlled process that is governed by the linear stochastic difference equation:
```
 x_k = Ax_k-1 + Bu_k-1 + w_k-1
 
```
with a measurement x_k that is
```
 z_k = Hx_k + v_k.
 
```
The random variables w_k and v_k 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

See Also:
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

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

Method Summary

Methods
Modifier and Type	Method and Description
`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

Class KalmanFilter

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

KalmanFilter

Method Detail

getStateDimension

getMeasurementDimension

getStateEstimation

getStateEstimationVector

getErrorCovariance

getErrorCovarianceMatrix

predict

predict

predict

correct

correct