clutterKF: Kalman Filter for Tracking in Clutter

In NTS: Nonlinear Time Series Analysis

Kalman Filter for Tracking in Clutter

Description

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

Usage

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

Arguments

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.

Value

The function returns a list with the following components:

xhat	the fitted location.
shat	the fitted speed.

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

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