|
||||||||||
PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object org.apache.commons.math3.filter.DefaultProcessModel
public class DefaultProcessModel
Default implementation of a ProcessModel
for the use with a KalmanFilter
.
Constructor Summary | |
---|---|
DefaultProcessModel(double[][] stateTransition,
double[][] control,
double[][] processNoise)
Create a new ProcessModel , taking double arrays as input parameters. |
|
DefaultProcessModel(double[][] stateTransition,
double[][] control,
double[][] processNoise,
double[] initialStateEstimate,
double[][] initialErrorCovariance)
Create a new ProcessModel , taking double arrays as input parameters. |
|
DefaultProcessModel(RealMatrix stateTransition,
RealMatrix control,
RealMatrix processNoise,
RealVector initialStateEstimate,
RealMatrix initialErrorCovariance)
Create a new ProcessModel , taking double arrays as input parameters. |
Method Summary | |
---|---|
RealMatrix |
getControlMatrix()
Returns the control matrix. |
RealMatrix |
getInitialErrorCovariance()
Returns the initial error covariance matrix. |
RealVector |
getInitialStateEstimate()
Returns the initial state estimation vector. |
RealMatrix |
getProcessNoise()
Returns the process noise matrix. |
RealMatrix |
getStateTransitionMatrix()
Returns the state transition matrix. |
Methods inherited from class java.lang.Object |
---|
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
Constructor Detail |
---|
public DefaultProcessModel(double[][] stateTransition, double[][] control, double[][] processNoise, double[] initialStateEstimate, double[][] initialErrorCovariance) throws NullArgumentException, NoDataException, DimensionMismatchException
ProcessModel
, taking double arrays as input parameters.
stateTransition
- the state transition matrixcontrol
- the control matrixprocessNoise
- the process noise matrixinitialStateEstimate
- the initial state estimate vectorinitialErrorCovariance
- the initial error covariance matrix
NullArgumentException
- if any of the input arrays is null
NoDataException
- if any row / column dimension of the input matrices is zero
DimensionMismatchException
- if any of the input matrices is non-rectangularpublic DefaultProcessModel(double[][] stateTransition, double[][] control, double[][] processNoise) throws NullArgumentException, NoDataException, DimensionMismatchException
ProcessModel
, taking double arrays as input parameters.
The initial state estimate and error covariance are omitted and will be initialized by the
KalmanFilter
to default values.
stateTransition
- the state transition matrixcontrol
- the control matrixprocessNoise
- the process noise matrix
NullArgumentException
- if any of the input arrays is null
NoDataException
- if any row / column dimension of the input matrices is zero
DimensionMismatchException
- if any of the input matrices is non-rectangularpublic DefaultProcessModel(RealMatrix stateTransition, RealMatrix control, RealMatrix processNoise, RealVector initialStateEstimate, RealMatrix initialErrorCovariance)
ProcessModel
, taking double arrays as input parameters.
stateTransition
- the state transition matrixcontrol
- the control matrixprocessNoise
- the process noise matrixinitialStateEstimate
- the initial state estimate vectorinitialErrorCovariance
- the initial error covariance matrixMethod Detail |
---|
public RealMatrix getStateTransitionMatrix()
getStateTransitionMatrix
in interface ProcessModel
public RealMatrix getControlMatrix()
getControlMatrix
in interface ProcessModel
public RealMatrix getProcessNoise()
KalmanFilter
every
prediction step, so implementations of this interface may return a modified process noise
depending on the current iteration step.
getProcessNoise
in interface ProcessModel
KalmanFilter.predict()
,
KalmanFilter.predict(double[])
,
KalmanFilter.predict(RealVector)
public RealVector getInitialStateEstimate()
Note: if the return value is zero, the Kalman filter will initialize the state estimation with a zero vector.
getInitialStateEstimate
in interface ProcessModel
public RealMatrix getInitialErrorCovariance()
Note: if the return value is zero, the Kalman filter will initialize the error covariance with the process noise matrix.
getInitialErrorCovariance
in interface ProcessModel
|
||||||||||
PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |