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)The function returns a list with components:
the fitted value.
the fitted value using Rao-Blackwellization.
the estimated indicators.
the estimated indicators using Rao-Blackwellization.
the Monte Carlo sample size.
the indicators.
the mean in the last iteration.
the covariance matrix of the Kalman filter components in the last iteration.
is the log weight of the last iteration.
the observations with T columns and ydim rows.
a list of parameter values. HH is the state coefficient matrix, WW*t(WW) is the state innovation covariance matrix,
VV*t(VV) is the covariance matrix of the observation noise, GG1 and GG2 are the observation coefficient matrix.
the dimension of the state variable x_t.
the dimension of the observation y_t.
a binary vector of length obs, reflecting the resampling schedule. resample.sch[i]= 1 indicating resample should be carried out at step i.
Tsay, R. and Chen, R. (2018). Nonlinear Time Series Analysis. John Wiley & Sons, New Jersey.