Package edu.wpi.first.math.estimator

Class KalmanFilter<States extends Num,Inputs extends Num,Outputs extends Num>

java.lang.Object

edu.wpi.first.math.estimator.KalmanFilter<States,Inputs,Outputs>

Type Parameters:

States - Number of states.

Inputs - Number of inputs.

Outputs - Number of outputs.

All Implemented Interfaces:

KalmanTypeFilter<States,Inputs,Outputs>

public class KalmanFilter<States extends Num,Inputs extends Num,Outputs extends Num>
extends Object
implements KalmanTypeFilter<States,Inputs,Outputs>

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".

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 Kalman filter with the given plant.

Method Summary

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.
void	correct(Matrix<Inputs,N1> u, Matrix<Outputs,N1> y, Matrix<Outputs,Outputs> R)	Correct the state estimate x-hat using the measurements in y.
Matrix<States,States>	getP()	Returns the error covariance matrix P.
double	getP(int row, int col)	Returns an element of the error covariance matrix P.
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()	Resets the observer.
void	setP(Matrix<States,States> newP)	Sets the entire error covariance matrix P.
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.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Details

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 Kalman filter with the given plant.

See https://docs.wpilib.org/en/stable/docs/software/advanced-controls/state-space/state-space-observers.html#process-and-measurement-noise-covariance-matrices for how to select the standard deviations.

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.

Throws:

IllegalArgumentException - If the system is unobservable.

Method Details

getP

public Matrix<States,States> getP()

Returns the error covariance matrix P.

Specified by:

getP in interface KalmanTypeFilter<States extends Num,Inputs extends Num,Outputs extends Num>

Returns:

the error covariance matrix P.

getP

public double getP(int row, int col)

Returns an element of the error covariance matrix P.

Specified by:

getP in interface KalmanTypeFilter<States extends Num,Inputs extends Num,Outputs extends Num>

Parameters:

row - Row of P.

col - Column of P.

Returns:

the value of the error covariance matrix P at (i, j).

setP

public void setP(Matrix<States,States> newP)

Sets the entire error covariance matrix P.

Specified by:

setP in interface KalmanTypeFilter<States extends Num,Inputs extends Num,Outputs extends Num>

Parameters:

newP - The new value of P to use.

getXhat

public Matrix<States,N1> getXhat()

Returns the state estimate x-hat.

Specified by:

getXhat in interface KalmanTypeFilter<States extends Num,Inputs extends Num,Outputs extends Num>

Returns:

the state estimate x-hat.

getXhat

public double getXhat(int row)

Returns an element of the state estimate x-hat.

Specified by:

getXhat in interface KalmanTypeFilter<States extends Num,Inputs extends Num,Outputs extends Num>

Parameters:

row - Row of x-hat.

Returns:

the value of the state estimate x-hat at that row.

setXhat

public void setXhat(Matrix<States,N1> xHat)

Set initial state estimate x-hat.

Specified by:

setXhat in interface KalmanTypeFilter<States extends Num,Inputs extends Num,Outputs extends Num>

Parameters:

xHat - The state estimate x-hat.

setXhat

public void setXhat(int row, double value)

Set an element of the initial state estimate x-hat.

Specified by:

setXhat in interface KalmanTypeFilter<States extends Num,Inputs extends Num,Outputs extends Num>

Parameters:

row - Row of x-hat.

value - Value for element of x-hat.

reset

public final void reset()

Description copied from interface: KalmanTypeFilter

Resets the observer.

Specified by:

reset in interface KalmanTypeFilter<States extends Num,Inputs extends Num,Outputs extends Num>

predict

public void predict(Matrix<Inputs,N1> u, double dtSeconds)

Project the model into the future with a new control input u.

Specified by:

predict in interface KalmanTypeFilter<States extends Num,Inputs extends Num,Outputs extends Num>

Parameters:

u - New control input from controller.

dtSeconds - Timestep for prediction.

correct

public void correct(Matrix<Inputs,N1> u, Matrix<Outputs,N1> y)

Correct the state estimate x-hat using the measurements in y.

Specified by:

correct in interface KalmanTypeFilter<States extends Num,Inputs extends Num,Outputs extends Num>

Parameters:

u - Same control input used in the predict step.

y - Measurement vector.

correct

public void correct(Matrix<Inputs,N1> u, Matrix<Outputs,N1> y, Matrix<Outputs,Outputs> R)

Correct the state estimate x-hat using the measurements in y.

This is useful for when the measurement noise covariances vary.

Parameters:

u - Same control input used in the predict step.

y - Measurement vector.

R - Continuous measurement noise covariance matrix.