public class ProcessEstimate extends Object
The estimate always contains time, state and covariance. These data are the only ones needed to start a Kalman filter. Once a filter has been started and produces new estimates, these new estimates will always contain a state transition matrix and if the measurement has not been ignored, they will also contain measurement Jacobian, innovation covariance and Kalman gain.
Constructor and Description |
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ProcessEstimate(double time,
RealVector state,
RealMatrix covariance)
Simple constructor.
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ProcessEstimate(double time,
RealVector state,
RealMatrix covariance,
RealMatrix stateTransitionMatrix,
RealMatrix measurementJacobian,
RealMatrix innovationCovariance,
RealMatrix kalmanGain)
Simple constructor.
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Modifier and Type | Method and Description |
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RealMatrix |
getCovariance()
Get the state covariance.
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RealMatrix |
getInnovationCovariance()
Get the innovation covariance matrix.
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RealMatrix |
getKalmanGain()
Get the Kalman gain matrix.
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RealMatrix |
getMeasurementJacobian()
Get the Jacobian of the measurement with respect to the state (H matrix).
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RealVector |
getState()
Get the state vector.
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RealMatrix |
getStateTransitionMatrix()
Get state transition matrix between previous state and estimated (but not yet corrected) state.
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double |
getTime()
Get the process time.
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public ProcessEstimate(double time, RealVector state, RealMatrix covariance)
This constructor sets state transition matrix, covariance matrix H, innovation covariance matrix and Kalman gain k to null.
time
- process time (typically the time or index of a measurement)state
- state vectorcovariance
- state covariancepublic ProcessEstimate(double time, RealVector state, RealMatrix covariance, RealMatrix stateTransitionMatrix, RealMatrix measurementJacobian, RealMatrix innovationCovariance, RealMatrix kalmanGain)
time
- process time (typically the time or index of a measurement)state
- state vectorcovariance
- state covariancestateTransitionMatrix
- state transition matrix between previous state and estimated (but not yet corrected) statemeasurementJacobian
- Jacobian of the measurement with respect to the stateinnovationCovariance
- innovation covariance matrix, defined as \(h.P.h^T + r\), may be nullkalmanGain
- Kalman Gain matrix, may be nullpublic double getTime()
public RealVector getState()
public RealMatrix getCovariance()
public RealMatrix getStateTransitionMatrix()
public RealMatrix getMeasurementJacobian()
public RealMatrix getInnovationCovariance()
public RealMatrix getKalmanGain()
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