avar (version 0.1.3)

avlr: Computes the Allan Variance Linear Regression estimator

Description

Estimate the parameters of time series models based on the Allan Variance Linear Regression (AVLR) approach

Usage

avlr(x, ...)
# S3 method for default
avlr(
  x,
  qn = NULL,
  wn = NULL,
  rw = NULL,
  dr = NULL,
  ci = FALSE,
  B = 100,
  alpha = 0.05,
  ...
)
# S3 method for imu_avar
avlr(
  x,
  qn_gyro = NULL,
  wn_gyro = NULL,
  rw_gyro = NULL,
  dr_gyro = NULL,
  qn_acc = NULL,
  wn_acc = NULL,
  rw_acc = NULL,
  dr_acc = NULL,
  B = 100,
  alpha = 0.05,
  ...
)

Value

If the input x is a vec, then the function returns a list that contains:

• "estimates": The estimated value of the parameters.

• "implied_ad": The Allan deviation implied by the estimated parameters.

• "implied_ad_decomp": The Allan deviation implied by the estimated parameters for each individual model (if more than one is specified).

• "av": The avar object computed from the provided data.

If the input x is of the class imu_avar, then the function returns a list that contains:

• "gyro": The estimation results correseponding to the gyroscope component.

• "acc": The estimation results correseponding to the accelerometer component.

• "imu_av": The imu_avar object computed based on the IMU data.

Arguments

x

A vec of time series observations or an imu object.

...

Further arguments passed to other methods.

qn

A vec specifying on which scales the parameters of a Quantization Noise (QN) should be computed.

wn

A vec specifying on which scales the parameters of a White Noise (WN) should be computed.

rw

A vec specifying on which scales the parameters of a Random Wakk (RW) should be computed.

dr

A vec specifying on which scales the parameters of a Drift (DR) should be computed.

ci

A boolean to compute parameter confidence intervals.

B

A double for the number of bootstrap replicates to compute the parameter confidence intervals.

alpha

A double defining the level of the confidence interval (1 - `alpha`).

qn_gyro

A vec specifying on which scales the parameters of a Quantization Noise (QN) should be computed for the gyroscope component.

wn_gyro

A vec specifying on which scales the parameters of a White Noise (WN) should be computed for the gyroscope component.

rw_gyro

A vec specifying on which scales the parameters of a Random Wakk (RW) should be computed for the gyroscope component.

dr_gyro

A vec specifying on which scales the parameters of a Drift (DR) should be computed for the gyroscope component.

qn_acc

A vec specifying on which scales the parameters of a Quantization Noise (QN) should be computed for the accelerometer component.

wn_acc

A vec specifying on which scales the parameters of a White Noise (WN) should be computed for the accelerometer component.

rw_acc

A vec specifying on which scales the parameters of a Random Wakk (RW) should be computed for the accelerometer component.

dr_acc

A vec specifying on which scales the parameters of a Drift (DR) should be computed for the accelerometer component.

Examples

# \donttest{
set.seed(999)

N = 100000
Xt = rnorm(N) + cumsum(rnorm(N, 0, 3e-3))

av = avar(Xt)
plot(av)

# Input time series
fit = avlr(Xt, wn = 1:8, rw = 11:15)
fit

# Input directly Allan variance
fit = avlr(av, wn = 1:8, rw = 11:15)
fit

# Plot functions
plot(fit)
plot(fit, decomp = TRUE)
plot(fit, decomp = TRUE, show_scales = TRUE)
# }