avarn: Allan Variance (incremental cluster size by unit).
In jhidalgocarrio/allanvar: Allan Variance Analysis

Description Usage Arguments Details Value Author(s) References Examples

The function avarn computes the Allan Variance of a set of values with a given constant sampling frequency. The input has to be the output rate/acceleration from the sensors. In this version of the Allan variance computation the number and size of cluster n has been computed as the maximum number of cluster into N recorded values, which is ceil [(N-1)/2].

1	avarn(values, freq)

`values`	Calibrated sensor output (i.e: angular velocity or acceleration)
`freq`	Sampling frequency rate in Hertz

It computes the Allan variance in the same way that avar function. The difference is that the number of cluster time and size are selected as the maximum number of cluster into N recorded values. However, Papoulis et.al recommend to estimate the amplitude of different noise components defined as in avar function. Therefore is recommended to use the avar in most of the cases, considering also computational cost.

Return an object of class data.frame containing the Allan Variance computation with the following fields:

`time`	Value of the cluster time.
`av`	The Allan variance value: variance among clusters of same size.
`error`	Error on the estimation: Uncertainty of the value.

Javier Hidalgo Carrio <javier.hidalgo_carrio@dfki.de>

Allan, D. W. (1966) Statistics of Atomic Frequency Standards Proceedings of IEEE, vol. 54, no. 2, pp. 221-230, Feb, 1966.

IEEE Std 952-1997 IEEE Standard Specification Format Guide and Test Procedure for Single Axis Interferometric Fiber Optic Gyros.

Papoulis, A and Unnikrisha,S (2002) Probability, Random Variables and Stochastic Processes Fourth Edition. McGraw Hill Series in electrical and Computer Engineering.

#Load data 
data(gyroz)

#Allan variance computation using avarn
avngyroz <- avarn(gyroz@.Data, frequency(gyroz))
plotav(avngyroz)
abline(1.0+log(avngyroz$time[1]), -1/2, col="green", lwd=4, lty=10)
abline(1.0+log(avngyroz$time[1]), 1/2, col="green", lwd=4, lty=10)
legend(0.11, 1e-03, c("Random Walk"))
legend(25, 1e-03, c("Rate Random Walk"))