MKFstep.fading: One Propagation Step under Mixture Kalman Filter for Fading...

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MKFstep.fadingR Documentation

One Propagation Step under Mixture Kalman Filter for Fading Channels

Description

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

Usage

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

Arguments

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, 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.

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.

Value

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.

References

Tsay, R. and Chen, R. (2018). Nonlinear Time Series Analysis. John Wiley & Sons, New Jersey.


NTS documentation built on Sept. 25, 2023, 1:08 a.m.

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