Sstep.Clutter: Sequential Monte Carlo for A Moving Target under Clutter...

In NTS: Nonlinear Time Series Analysis

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)

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