simToa: Sim TOA matrix for the supplied telemetryTrack

In baktoft/yaps: Track Estimation using YAPS (Yet Another Positioning Solver)

Sim TOA matrix for the supplied telemetryTrack

Description

Provides the TOA matrix for the specified telemetryTrack. Probability of NA (pNA) and observation noise (sigmaToa) can be specified.

Usage

simToa(telemetryTrack, hydros, pingType, sigmaToa, pNA, pMP, tempRes = NA)

Arguments

telemetryTrack	Dataframe obtained from simTelemetryTrack
hydros	Dataframe obtained from getHydros
pingType	Type of transmitter to simulate - either stable burst interval ('sbi'), random burst interval ('rbi') or random burst interval but where the random sequence is known a priori
sigmaToa	Detection uncertainty
pNA	Probability of missing detection 0-1
pMP	Probability of multipath propagated signal 0-1
tempRes	Temporal resolution of the hydrophone. PPM systems are typially 1/1000 sec. Other systems are as high as 1/19200 sec.

Value

List containing TOA matrix (toa) and matrix indicating, which obs are multipath (mp_mat)

Examples

library(yaps)
set.seed(42)
# Simulate true track of animal movement of n seconds
trueTrack <- simTrueTrack(model='crw', n = 1000, deltaTime=1, shape=1, 
	scale=0.5, addDielPattern=TRUE, ss='rw')

# Simulate telemetry observations from true track.
# Format and parameters depend on type of transmitter burst interval (BI).
pingType <- 'sbi'

if(pingType == 'sbi') { # stable BI
    sbi_mean <- 30; sbi_sd <- 1e-4;
    teleTrack <- simTelemetryTrack(trueTrack, pingType=pingType, sbi_mean=sbi_mean, sbi_sd=sbi_sd)
} else if(pingType == 'rbi'){ # random BI
    pingType <- 'rbi'; rbi_min <- 20; rbi_max <- 40;
    teleTrack <- simTelemetryTrack(trueTrack, pingType=pingType, rbi_min=rbi_min, rbi_max=rbi_max)
}

# Simulate hydrophone array
hydros <- simHydros(auto=TRUE, trueTrack=trueTrack)
toa_list <- simToa(teleTrack, hydros, pingType, sigmaToa=1e-4, pNA=0.25, pMP=0.01)
toa <- toa_list$toa

# Specify whether to use ss_data from measured water temperature (ss_data_what <- 'data') or 
#	to estimate ss in the model (ss_data_what <- 'est')
ss_data_what <- 'data'
if(ss_data_what == 'data') {ss_data <- teleTrack$ss} else {ss_data <- 0}


if(pingType == 'sbi'){
    inp <- getInp(hydros, toa, E_dist="Mixture", n_ss=10, pingType=pingType, sdInits=0, 
		ss_data_what=ss_data_what, ss_data=ss_data)
} else if(pingType == 'rbi'){
    inp <- getInp(hydros, toa, E_dist="Mixture", n_ss=10, pingType=pingType, sdInits=0, 
		rbi_min=rbi_min, rbi_max=rbi_max, ss_data_what=ss_data_what, ss_data=ss_data)
} 

pl <- c()
maxIter <- ifelse(pingType=="sbi", 500, 5000)
outTmb <- runYaps(inp, maxIter=maxIter, getPlsd=TRUE, getRep=TRUE)

# Estimates in pl
pl <- outTmb$pl
# Correcting for hydrophone centering
pl$X <- outTmb$pl$X + inp$inp_params$Hx0
pl$Y <- outTmb$pl$Y + inp$inp_params$Hy0


# Error estimates in plsd
plsd <- outTmb$plsd

# plot the resulting estimated track
plot(y~x, data=trueTrack, type="l", xlim=range(hydros$hx), ylim=range(hydros$hy), asp=1)
lines(y~x, data=teleTrack)
points(hy~hx, data=hydros, col="green", pch=20, cex=3)
lines(pl$Y~pl$X, col="red")

baktoft/yaps documentation built on Nov. 12, 2023, 2:30 p.m.