clutterKF: Kalman Filter for Tracking in Clutter

Description Usage Arguments Value References Examples

View source: R/SMC.r

Description

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

Usage

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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

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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 Aug. 6, 2020, 5:08 p.m.

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