Sstep.Clutter {NTS}

Sequential Monte Carlo for A Moving Target under Clutter Environment

Description

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

Usage

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

Arguments

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.

Value

The function returns a list with the following components:

xx

the new sample.

logww

the log weights.

References

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

Examples

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)