scalation
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scalation/scalation/scalation.modeling/scalation.modeling.forecasting/KalmanFilter

KalmanFilter

scalation.modeling.forecasting.KalmanFilter
class KalmanFilter(val f: MatrixD, val q: MatrixD, val h: MatrixD, val r: MatrixD, var x: VectorD, var p: MatrixD)

The KalmanFilter class provides a simple implementation of a Kalman filter. It is useful for smoothing noisy data and for providing better estimates of the state of a system.

Value parameters

f

the state transition matrix

h

the measurement matrix

p

the initial covariance matrix

q

the process noise covariance matrix

r

the measurement noise covariance matrix

x

the initial state vector

Attributes

Graph
Supertypes
class Object
trait Matchable
class Any

Members list

Value members

Concrete methods

def copyFilter(): KalmanFilter

Copy this Kalman Filter and return it.

Copy this Kalman Filter and return it.

Attributes

def predict(): Unit

Predict the state of the process at the next time point.

Predict the state of the process at the next time point.

Attributes

def solve(dt: Double): VectorD

Iteratively solve for x using predict and update phases.

Iteratively solve for x using predict and update phases.

Value parameters

dt

the time increment (delta t)

Attributes

def update(z: VectorD): Unit

Update the state and covariance estimates with the current and possibly noisy measurements

Update the state and covariance estimates with the current and possibly noisy measurements

Value parameters

z

current measurement/observation of the state

Attributes

Concrete fields

val f: MatrixD
val h: MatrixD
var p: MatrixD
val q: MatrixD
val r: MatrixD
val traj: MatrixD
var x: VectorD
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