avlr: Computes the Allan Variance Linear Regression estimator
In avar: Allan Variance

View source: R/avlr.R

avlr	R Documentation

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, ...)

## Default S3 method:
avlr(
  x,
  qn = NULL,
  wn = NULL,
  rw = NULL,
  dr = NULL,
  ci = FALSE,
  B = 100,
  alpha = 0.05,
  ...
)

## S3 method for class '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,
  ...
)

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.

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.

Examples


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)

avar documentation built on Aug. 29, 2023, 5:09 p.m.