navigation: Runs "IMU model evaluation" or "INS-GPS-Baro integrated...

In navigation: Analyze the Impact of Sensor Error Modelling on Navigation Performance

Runs "IMU model evaluation" or "INS-GPS-Baro integrated navigation (sensor fusion)"

Description

This function performs of the two following main tasks, base on the provided input. If a reference trajectory (traj.ref) is provided, it generates sensor data (IMU, GPS, Baro) corrupted by additive errors according to snsr.mdl, and performs navigation using KF.mdl as the sensor error model within the Kalman filter to evaluate how this particular model performs when navigating.

Usage

navigation(
  traj.ref,
  timing,
  snsr.mdl,
  KF.mdl,
  g = 9.8056,
  num.runs = 1,
  results.system = "ned",
  x_o = NULL,
  noProgressBar = FALSE,
  IC = NULL,
  imu_data = NULL,
  gps_data = NULL,
  baro_data = NULL,
  input.seed = 0,
  PhiQ_method = "exact",
  P_subsampling = 1,
  compute_PhiQ_each_n = 1,
  parallel.ncores = detectCores(all.tests = FALSE, logical = TRUE),
  tmpdir = tempdir()
)

Arguments

traj.ref	A trajectory object (see the documentation for make_trajectory), serving as the reference trajectory for generating sensor data and evaluating the error in navigation once performed. Only position and attitude data are required/considered, and velocity will be calculated from position.
timing	A timing object (see the documentation for make_timing) containing timing information such as start and end of navigation.
snsr.mdl	A sensor object (see the documentation for make_sensor) containing additive sensor error model to generate realistic sensor data.
KF.mdl	A sensor object (see the documentation for make_sensor) containing additive sensor error model to be used within the Kalman filter for navigation.
g	Gravitational acceleration.
num.runs	Number of times the sensor data generation and navigation is performed (Monte-Carlo simulation).
results.system	The coordinate system (ned/ellipsoidal) in which the results are reported (see the documentation for make_trajectory).
x_o	Origin of the fixed ned frame.
noProgressBar	A bolean specifying if there should not be a progress bar.
IC	Initial conditions. See the examples for the format.
imu_data	IMU data. See the examples for the format.
gps_data	GPS data. See the examples for the format.
baro_data	Baro data. See the examples for the format.
input.seed	Seed for the random number generator. Actual seed is computed as input.seed * num.runs + run
PhiQ_method	String that specify the method to compute Phi and Q matrices, can be "exact" or the order of the Taylor expansions to use.
P_subsampling	(memory optimization) store only one sample of the P matrix each P_subsampling time instants.
compute_PhiQ_each_n	Specify the interval of IMU measurements between each computation of PhiQ.
parallel.ncores	The number of cores to be used for parallel Monte-Carlo runs.
tmpdir	Where to store temporary navigation output. It should not be mapped on a filesystem which lives in RAM.

Value

An object of navigation class containing the reference trajectory, fused trajectory, sensor data, covariance matrix, and time.

Author(s)

Davide Cucci, Lionel Voirol, Mehran Khaghani, Stéphane Guerrier

Examples

# load data
data("lemniscate_traj_ned")
head(lemniscate_traj_ned)
traj <- make_trajectory(data = lemniscate_traj_ned, system = "ned")
timing <- make_timing(
  nav.start = 0, # time at which to begin filtering
  nav.end = 15,
  freq.imu = 100, # frequency of the IMU, can be slower wrt trajectory frequency
  freq.gps = 1, # GNSS frequency
  freq.baro = 1, # barometer frequency (to disable, put it very low, e.g. 1e-5)
  gps.out.start = 8, # to simulate a GNSS outage, set a time before nav.end
  gps.out.end = 13
)
# create sensor for noise data generation
snsr.mdl <- list()
# this uses a model for noise data generation
acc.mdl <- WN(sigma2 = 5.989778e-05) +
  AR1(phi = 9.982454e-01, sigma2 = 1.848297e-10) +
  AR1(phi = 9.999121e-01, sigma2 = 2.435414e-11) +
  AR1(phi = 9.999998e-01, sigma2 = 1.026718e-12)
gyr.mdl <- WN(sigma2 = 1.503793e-06) +
  AR1(phi = 9.968999e-01, sigma2 = 2.428980e-11) +
  AR1(phi = 9.999001e-01, sigma2 = 1.238142e-12)
snsr.mdl$imu <- make_sensor(
  name = "imu",
  frequency = timing$freq.imu,
  error_model1 = acc.mdl,
  error_model2 = gyr.mdl
)
# RTK-like GNSS
gps.mdl.pos.hor <- WN(sigma2 = 0.025^2)
gps.mdl.pos.ver <- WN(sigma2 = 0.05^2)
gps.mdl.vel.hor <- WN(sigma2 = 0.01^2)
gps.mdl.vel.ver <- WN(sigma2 = 0.02^2)
snsr.mdl$gps <- make_sensor(
  name = "gps",
  frequency = timing$freq.gps,
  error_model1 = gps.mdl.pos.hor,
  error_model2 = gps.mdl.pos.ver,
  error_model3 = gps.mdl.vel.hor,
  error_model4 = gps.mdl.vel.ver
)
# Barometer
baro.mdl <- WN(sigma2 = 0.5^2)
snsr.mdl$baro <- make_sensor(
  name = "baro",
  frequency = timing$freq.baro,
  error_model1 = baro.mdl
)
# define sensor for Kalmna filter
KF.mdl <- list()
# make IMU sensor
KF.mdl$imu <- make_sensor(
  name = "imu",
  frequency = timing$freq.imu,
  error_model1 = acc.mdl,
  error_model2 = gyr.mdl
)
KF.mdl$gps <- snsr.mdl$gps
KF.mdl$baro <- snsr.mdl$baro
# perform navigation simulation
num.runs <- 5 # number of Monte-Carlo simulations
res <- navigation(
  traj.ref = traj,
  timing = timing,
  snsr.mdl = snsr.mdl,
  KF.mdl = KF.mdl,
  num.runs = num.runs,
  noProgressBar = TRUE,
  PhiQ_method = "1",
  # order of the Taylor expansion of the matrix exponential
  # used to compute Phi and Q matrices
  compute_PhiQ_each_n = 10,
  # compute new Phi and Q matrices every n IMU steps (execution time optimization)
  parallel.ncores = 1,
  P_subsampling = timing$freq.imu
) # keep one covariance every second

navigation documentation built on May 31, 2023, 8:39 p.m.