MKFstep.fading: One Propagation Step under Mixture Kalman Filter for Fading...
In NTS: Nonlinear Time Series Analysis

MKFstep.fading

R Documentation

One Propagation Step under Mixture Kalman Filter for Fading Channels

This function implements the one propagation step under mixture Kalman filter for fading channels.

MKFstep.fading(mm, II, mu, SS, logww, yyy, par, xdim, ydim, resample)

`mm`	the Monte Carlo sample size.
`II`	the indicators.
`mu`	the mean in the last iteration.
`SS`	the covariance matrix of the Kalman filter components in the last iteration.
`logww`	is the log weight of the last iteration.
`yyy`	the observations with `T` columns and `ydim` rows.
`par`	a list of parameter values. `HH` is the state coefficient matrix, `WWt(WW)` is the state innovation covariance matrix, `VVt(VV)` is the covariance matrix of the observation noise, `GG1` and `GG2` are the observation coefficient matrix.
`xdim`	the dimension of the state variable `x_t`.
`ydim`	the dimension of the observation `y_t`.
`resample`	a binary vector of length `obs`, reflecting the resampling schedule. resample.sch[i]= 1 indicating resample should be carried out at step `i`.

The function returns a list with components:

`xhat`	the fitted value.
`xhatRB`	the fitted value using Rao-Blackwellization.
`Iphat`	the estimated indicators.
`IphatRB`	the estimated indicators using Rao-Blackwellization.