# Class SteadyStateKalmanFilter<States extends Num,Inputs extends Num,Outputs extends Num>

java.lang.Object
Type Parameters:
States - Number of states.
Inputs - Number of inputs.
Outputs - Number of outputs.

public class SteadyStateKalmanFilter<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.

This class assumes predict() and correct() are called in pairs, so the Kalman gain converges to a steady-state value. If they aren't, use KalmanFilter instead.

For more on the underlying math, read https://file.tavsys.net/control/controls-engineering-in-frc.pdf chapter 9 "Stochastic control theory".

• ## Method Details

• ### reset

public final void reset()
Resets the observer.
• ### getK

public  getK()
Returns the steady-state Kalman gain matrix K.
Returns:
The steady-state Kalman gain matrix K.
• ### 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.
• ### setXhat

public void setXhat(Matrix<States,N1> xhat)
Set initial state estimate x-hat.
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.
Parameters:
row - Row of x-hat.
value - Value for element of x-hat.
• ### getXhat

public  getXhat()
Returns the state estimate x-hat.
Returns:
The state estimate x-hat.
• ### 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 that row.
• ### 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.
• ### correct

public void correct(Matrix<Inputs,N1> u, Matrix<Outputs,N1> y)
Correct the state estimate x-hat using the measurements in y.
Parameters:
u - Same control input used in the last predict step.
y - Measurement vector.