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java.lang.Objectorg.apache.commons.math3.filter.DefaultMeasurementModel
public class DefaultMeasurementModel
Default implementation of a MeasurementModel for the use with a KalmanFilter.
| Constructor Summary | |
|---|---|
DefaultMeasurementModel(double[][] measMatrix,
double[][] measNoise)
Create a new MeasurementModel, taking double arrays as input parameters for the
respective measurement matrix and noise. |
|
DefaultMeasurementModel(RealMatrix measMatrix,
RealMatrix measNoise)
Create a new MeasurementModel, taking RealMatrix objects
as input parameters for the respective measurement matrix and noise. |
|
| Method Summary | |
|---|---|
RealMatrix |
getMeasurementMatrix()
Returns the measurement matrix. |
RealMatrix |
getMeasurementNoise()
Returns the measurement noise matrix. |
| Methods inherited from class java.lang.Object |
|---|
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Constructor Detail |
|---|
public DefaultMeasurementModel(double[][] measMatrix,
double[][] measNoise)
throws NullArgumentException,
NoDataException,
DimensionMismatchException
MeasurementModel, taking double arrays as input parameters for the
respective measurement matrix and noise.
measMatrix - the measurement matrixmeasNoise - the measurement noise matrix
NullArgumentException - if any of the input matrices 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 DefaultMeasurementModel(RealMatrix measMatrix,
RealMatrix measNoise)
MeasurementModel, taking RealMatrix objects
as input parameters for the respective measurement matrix and noise.
measMatrix - the measurement matrixmeasNoise - the measurement noise matrix| Method Detail |
|---|
public RealMatrix getMeasurementMatrix()
getMeasurementMatrix in interface MeasurementModelpublic RealMatrix getMeasurementNoise()
KalmanFilter every
correction step, so implementations of this interface may return a modified measurement noise
depending on the current iteration step.
getMeasurementNoise in interface MeasurementModelKalmanFilter.correct(double[]),
KalmanFilter.correct(org.apache.commons.math3.linear.RealVector)
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