# simPassiveSonar: Simulate A Sample Trajectory In NTS: Nonlinear Time Series Analysis

## Description

The function generates a sample trajectory of the target and the corresponding observations with sensor locations at (0,0) and (20,0).

## Usage

 `1` ```simPassiveSonar(nn = 200, q, r, start, seed) ```

## Arguments

 `nn` sample size. `q` contains the information about the covariance of the noise. `r` contains the information about `V`, where `V*t(V)` is the covariance matrix of the observation noise. `start` the initial value. `seed` the seed of random number generator.

## Value

The function returns a list with components:

 `xx` the state data. `yy` the observed data. `H` the state coefficient matrix. `W` `W*t(W)` is the state innovation covariance matrix. `V` `V*t(V)` is the observation noise covariance matrix.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16``` ```s2 <- 20 #second sonar location at (s2,0) q <- c(0.03,0.03) r <- c(0.02,0.02) nobs <- 200 start <- c(10,10,0.01,0.01) H <- c(1,0,1,0,0,1,0,1,0,0,1,0,0,0,0,1) H <- matrix(H,ncol=4,nrow=4,byrow=TRUE) W <- c(0.5*q, 0,0, 0.5*q,q,0,0,q) W <- matrix(W,ncol=2,nrow=4,byrow=TRUE) V <- diag(r) mu0 <- start SS0 <- diag(c(1,1,1,1))*0.01 simu_out <- simPassiveSonar(nobs,q,r,start,seed=20) yy<- simu_out\$yy tt<- 100:200 plot(simu_out\$xx[1,tt],simu_out\$xx[2,tt],xlab='x',ylab='y') ```

NTS documentation built on Aug. 6, 2020, 5:08 p.m.