clutterKF: Kalman Filter for Tracking in Clutter
In ConvFuncTimeSeries/test_t: Nonlinear time series analysis

Description Usage Arguments Value References Examples

This function implments Kalman filter to track a moving target under clutter environment with known indicators.

1	clutterKF(nobs, ssw, ssv, yy, ii)

`nobs`	the number of observations.
`ssw`	the standard deviation in the state equation.
`ssv`	the standard deviation for the observation noise.
`yy`	the data.
`ii`	the indicators.

The function returns a list with the following components:

`xhat`	the fitted location.
`shat`	the fitted speed.

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)
outKF <- clutterKF(nobs,ssw,ssv,simu$yy,simu$ii)