# Sstep.Clutter: Sequential Monte Carlo for A Moving Target under Clutter... In NTS: Nonlinear Time Series Analysis

 Sstep.Clutter R Documentation

## 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.