Sstep.Clutter: Sequential Monte Carlo for A Moving Target under Clutter...
In ConvFuncTimeSeries/test_t: Nonlinear time series analysis

Description Usage Arguments Value References Examples

The function performs one step propagation using the sequential Monte Carlo method with partial state proposal for tracking in clutter problem.

1	Sstep.Clutter(mm, xx, logww, yyy, par, xdim, ydim)

`mm`	the Monte Carlo sample size `m`.
`xx`	the sample 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 varible.
`ydim`	the dimension of the observation.

The function returns a list with the following components:

`xx`	the new sample.
`logww`	the log weights.

Tsay, R. and Chen, R. (2019). Nonlinear Time Series Analysis. Wiley, New Jersey.

nobs <- 100; pd <- 0.95; ssw <- 0.1; ssv <- 0.5;
xx0 <- 0; ss0 <- 0.1; nyy <- 50;
yrange <- c(-80,80); xdim <- 2; ydim <- nyy; 
simu <- simuTargetClutter(nobs,pd,ssw,ssv,xx0,ss0,nyy,yrange)
resample.sch <- rep(1,nobs)
mm <- 10000
yr <- yrange[2]-yrange[1]
par <- list(ssw=ssw,ssv=ssv,nyy=nyy,pd=pd,yr=yr)
yr<- yrange[2]-yrange[1]
xx.init <- matrix(nrow=2,ncol=mm)
xx.init[1,] <- yrange[1]+runif(mm)*yr
xx.init[2,] <- rep(0.1,mm)
out <- SMC(Sstep.Clutter,nobs,simu$yy,mm,par,xx.init,xdim,ydim,resample.sch)