KF {RobKF} | R Documentation |
The classical Kalman filter.
KF(Y, mu_0, Sigma_0 = NULL, A, C, Sigma_Add, Sigma_Inn, epsilon = 1e-06)
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. |
An rkf S3 class.
Kalman RE (1960). “A New Approach to Linear Filtering and Prediction Problems.” Transactions of the ASME–Journal of Basic Engineering, 82(Series D), 35–45.
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)