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java.lang.Objectorg.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. |
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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-rectangular
public 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-rectangular
public 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 matrix| Method Detail |
|---|
public RealMatrix getStateTransitionMatrix()
getStateTransitionMatrix in interface ProcessModelpublic RealMatrix getControlMatrix()
getControlMatrix in interface ProcessModelpublic 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 ProcessModelKalmanFilter.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 ProcessModelpublic 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
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