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Estimating the Allan variance in the presence of long periods of missing data and outliers
No full textThe ability of the Allan variance (AVAR) to identify and estimate the typical clock noise is
widely accepted, and its use is recommended by international standards. Recently, a
time-varying version called Dynamic Allan variance (DAVAR) was suggested and exploited.
Currently, the AVAR is commonly used in applications to space and satellite systems, in
particular in monitoring the clocks of the Global Positioning System, and also in the
framework of the European project Galileo. In these applications stability estimation, either
AVAR or DAVAR (or other similar variances), presents some peculiar aspects which are not
commonly encountered when the clock data are measured in a laboratory. In particular, data
from space clocks may typically present outliers and missing values. Hence, special attention
has to be paid when dealing with such experimental measurements.
In this work we propose an estimation algorithm and its implementation in a robust
software code (in MATLAB® language) able to estimate the AVAR in the case of missing data,
unequally spaced data, outliers, and with long periods of missing observation, so that the Allan
variance estimates turn out unbiased and with the maximum use of all the available data
Satellite Clocks Characterization and Monitoring for Global Navigation Satellite Systems
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