Package edu.wpi.first.math.estimator
Class KalmanFilter<States extends Num,Inputs extends Num,Outputs extends Num>
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- edu.wpi.first.math.estimator.KalmanFilter<States,Inputs,Outputs>
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public class KalmanFilter<States extends Num,Inputs extends Num,Outputs extends Num> extends Object
A Kalman filter combines predictions from a model and measurements to give an estimate of the true system state. This is useful because many states cannot be measured directly as a result of sensor noise, or because the state is "hidden".Kalman filters use a K gain matrix to determine whether to trust the model or measurements more. Kalman filter theory uses statistics to compute an optimal K gain which minimizes the sum of squares error in the state estimate. This K gain is used to correct the state estimate by some amount of the difference between the actual measurements and the measurements predicted by the model.
For more on the underlying math, read https://file.tavsys.net/control/controls-engineering-in-frc.pdf chapter 9 "Stochastic control theory".
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Constructor Summary
Constructors Constructor Description KalmanFilter(Nat<States> states, Nat<Outputs> outputs, LinearSystem<States,Inputs,Outputs> plant, Matrix<States,N1> stateStdDevs, Matrix<Outputs,N1> measurementStdDevs, double dtSeconds)
Constructs a state-space observer with the given plant.
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description void
correct(Matrix<Inputs,N1> u, Matrix<Outputs,N1> y)
Correct the state estimate x-hat using the measurements in y.Matrix<States,Outputs>
getK()
Returns the steady-state Kalman gain matrix K.double
getK(int row, int col)
Returns an element of the steady-state Kalman gain matrix K.Matrix<States,N1>
getXhat()
Returns the state estimate x-hat.double
getXhat(int row)
Returns an element of the state estimate x-hat.void
predict(Matrix<Inputs,N1> u, double dtSeconds)
Project the model into the future with a new control input u.void
reset()
void
setXhat(int row, double value)
Set an element of the initial state estimate x-hat.void
setXhat(Matrix<States,N1> xhat)
Set initial state estimate x-hat.
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Constructor Detail
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KalmanFilter
public KalmanFilter(Nat<States> states, Nat<Outputs> outputs, LinearSystem<States,Inputs,Outputs> plant, Matrix<States,N1> stateStdDevs, Matrix<Outputs,N1> measurementStdDevs, double dtSeconds)
Constructs a state-space observer with the given plant.- Parameters:
states
- A Nat representing the states of the system.outputs
- A Nat representing the outputs of the system.plant
- The plant used for the prediction step.stateStdDevs
- Standard deviations of model states.measurementStdDevs
- Standard deviations of measurements.dtSeconds
- Nominal discretization timestep.
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Method Detail
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reset
public void reset()
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getK
public Matrix<States,Outputs> getK()
Returns the steady-state Kalman gain matrix K.- Returns:
- The steady-state Kalman gain matrix K.
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getK
public double getK(int row, int col)
Returns an element of the steady-state Kalman gain matrix K.- Parameters:
row
- Row of K.col
- Column of K.- Returns:
- the element (i, j) of the steady-state Kalman gain matrix K.
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setXhat
public void setXhat(Matrix<States,N1> xhat)
Set initial state estimate x-hat.- Parameters:
xhat
- The state estimate x-hat.
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setXhat
public void setXhat(int row, double value)
Set an element of the initial state estimate x-hat.- Parameters:
row
- Row of x-hat.value
- Value for element of x-hat.
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getXhat
public Matrix<States,N1> getXhat()
Returns the state estimate x-hat.- Returns:
- The state estimate x-hat.
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getXhat
public double getXhat(int row)
Returns an element of the state estimate x-hat.- Parameters:
row
- Row of x-hat.- Returns:
- the state estimate x-hat at i.
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predict
public void predict(Matrix<Inputs,N1> u, double dtSeconds)
Project the model into the future with a new control input u.- Parameters:
u
- New control input from controller.dtSeconds
- Timestep for prediction.
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