KF: The classical Kalman filter

In RobKF: Innovative and/or Additive Outlier Robust Kalman Filtering

Description

The classical Kalman filter.

Usage

KF(Y, mu_0, Sigma_0 = NULL, A, C, Sigma_Add, Sigma_Inn, epsilon = 1e-06)

Arguments

Y	A list of matrices containing the observations to be filtered.
mu_0	A matrix indicating the mean of the prior for the hidden states.
Sigma_0	A matrix indicating the variance of the prior for the hidden states. It defaults to the limit of the variance of the Kalman filter.
A	A matrix giving the updates for the hidden states.
C	A matrix mapping the hidden states to the observed states.
Sigma_Add	A positive definite matrix giving the additive noise covariance.
Sigma_Inn	A positive definite matrix giving the innovative noise covariance.
epsilon	A positive numeric giving the precision to which the limit of the covariance is to be computed. It defaults to 0.000001.

Value

An rkf S3 class.

References

\insertRef

Kalman1960RobKF

Examples

library(RobKF)

set.seed(2019)

A = matrix(c(1), nrow = 1, ncol = 1)
C = matrix(c(1), nrow = 1, ncol = 1)

Sigma_Inn = diag(1,1)*0.01
Sigma_Add = diag(1,1)

mu_0 = matrix(0,nrow=1,ncol=1)

Y_list = Generate_Data(1000,A,C,Sigma_Add,Sigma_Inn,mu_0)

Output = KF(Y_list,mu_0,Sigma_0=NULL,A,C,Sigma_Add,Sigma_Inn)

plot(Output)

RobKF documentation built on July 15, 2021, 5:06 p.m.