track3D: Reconstruct a track from pitch, heading and depth data, given...

In tagtools: Work with Data from High-Resolution Biologging Tags

Reconstruct a track from pitch, heading and depth data, given a starting position

Description

The track3D function will use data from a tag to reconstruct a track by fitting a state space model using a Kalman filter. If no x,y observations are provided then this corresponds to a pseudo-track obtained via dead reckoning and extreme care is required in interpreting the results.

Usage

track3D(
  z,
  phi,
  psi,
  sf,
  r = 0.001,
  q1p = 0.02,
  q2p = 0.08,
  q3p = 1.6e-05,
  tagonx,
  tagony,
  enforce = TRUE,
  x,
  y
)

Arguments

z	A vector with depth over time (in meters, an observation)
phi	A vector with pitch over time (in Radians, assumed as a known covariate)
psi	A vector with heading over time (in Radians, assumed as a known covariate)
sf	A scalar defining the sampling rate (in Hz)
r	Observation error
q1p	speed state error
q2p	depth state error
q3p	x and y state error
tagonx	Easting of starting position (in meters, so requires projected data)
tagony	Northing of starting position (in meters, so requires projected data)
enforce	If TRUE (the default), then speed and depth are kept strictly positive
x	Direct observations of Easting (in meters, so requires projected data)
y	Direct observations of Northing (in meters, so requires projected data)

Value

A list with 10 elements:

• p: the smoothed speeds

• fit.ks: the fitted speeds

• fit.kd: the fitted depths

• fit.xs: the fitted xs

• fit.ys: the fitted ys

• fit.rd: the smoothed depths

• fit.rx: the smoothed xs

• fit.ry: the smoothed ys

• fit.kp: the kalman a posteriori state covariance

• fit.ksmo: the kalman smoother variance

Note

Output sampling rate is the same as the input sampling rate.

Frame: This function assumes a [north,east,up] navigation frame and a [forward,right,up] local frame. In these frames, a positive pitch angle is an anti-clockwise rotation around the y-axis. A positive roll angle is a clockwise rotation around the x-axis. A descending animal will have a negative pitch angle while an animal rolled with its right side up will have a positive roll angle.

This function output can be quite sensitive to the inputs used, namely those that define the relative weight given to the existing data, in particular regarding (x,y)=(lat,long); increasing q3p, the (x,y) state variance, will increase the weight given to independent observations of (x,y), say from GPS readings

See Also

m2h,a2pr

Examples

p <- a2pr(A = beaked_whale$A$data)
h <- m2h(M = beaked_whale$M$data, A = beaked_whale$A$data)
track <- track3D(z = beaked_whale$P$data, phi = p$p, 
psi = h$h, sf = beaked_whale$A$sampling_rate, 
r = 0.001, q1p = 0.02, q2p = 0.08, q3p = 1.6e-05, 
tagonx = 1000, tagony = 1000, enforce = TRUE, x = NA, y = NA)
oldpar <- graphics::par(no.readonly = TRUE)
graphics::par(mfrow = c(2, 1), mar = c(4, 4, 0.5, 0.5))
plot(-beaked_whale$P$data, pch = ".", ylab = "Depth (m)", 
xlab = "Time")
plot(track$fit.rx, track$fit.ry, xlab = "X", 
ylab = "Y", pch = ".")
points(track$fit.rx[c(1, length(track$fit.rx))], 
track$fit.ry[c(1, length(track$fit.rx))], pch = 21, bg = 5:6)
legend("bottomright", cex = 0.7, legend = c("Start", "End"), 
col = c(5, 6), pt.bg = c(5, 6), pch = c(21, 21))
graphics::par(oldpar)

tagtools documentation built on June 28, 2024, 5:07 p.m.