KF {RobKF}

The classical Kalman filter

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

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.

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)