Sstep.Clutter.Full: Sequential Importance Sampling under Clutter Environment

In NTS: Nonlinear Time Series Analysis

Sequential Importance Sampling under Clutter Environment

Description

This function performs one step propagation using the sequential importance sampling with full information proposal distribution under clutter environment.

Usage

Sstep.Clutter.Full(mm, xx, logww, yyy, par, xdim, ydim, resample.sch)

Arguments

mm	the Monte Carlo sample size m.
xx	the samples in the last iteration.
logww	the log weight in the last iteration.
yyy	the observations.
par	a list of parameter values (ssw,ssv,pd,nyy,yr), where ssw is the standard deviation in the state equation, ssv is the standard deviation for the observation noise, pd is the probability to observe the true signal, nyy the dimension of the data, and yr is the range of the data.
xdim	the dimension of the state variable x_t.
ydim	the dimension of the observation y_t.
resample.sch	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 the following components:

xx	the new sample.
logww	the log weights.
r.index	resample index, if resample.sch=1.

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.