AORKF_t {RobKF} | R Documentation |
An additive outlier robust Kalman filter, based on the work by Agamennoni et al. (2018). This function assumes that the additions are potentially polluted by a heavy tailed process, which is approximated by a t-distribution. Variational inference is used to approximate the posterior.
AORKF_t(
Y,
mu_0,
Sigma_0 = NULL,
A,
C,
Sigma_Add,
Sigma_Inn,
s = 2,
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. |
s |
A numeric giving the shape of the t-distribution to be considered. It defaults to 2. |
epsilon |
A positive numeric giving the precision to which the limit of the covariance, and the variational inferences is to be computed. It defaults to 0.000001. |
An rkf S3 class.
Agamennoni G, Nieto JI, Nebot EM (2011). “An outlier-robust Kalman filter.” In 2011 IEEE International Conference on Robotics and Automation, 1551–1558. IEEE.
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,anomaly_loc = c(100,400,700),
anomaly_type = c("Add","Add","Add"),anomaly_comp = c(1,1,1),
anomaly_strength = c(10,10,10))
Output = AORKF_t(Y_list,mu_0,Sigma_0=NULL,A,C,Sigma_Add,Sigma_Inn)
plot(Output,conf_level = 0.9999)