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Characterizing Frequency Stability Measurements having Multiple Data Gaps

Published

Author(s)

David A. Howe, Noah Schlossberger

Abstract

Time series measurements with data gaps (dead times) prevent accurate computations of frequency variances such as the Allan variance (AVAR) and its square-root ADEV. To extract frequency distributions, data must be sequentially ordered and equally spaced. Data gaps, particularly large ones, make ADEV measurements unreliable. Gap imputation by interpolation, zero-padding, or adjoining live segments fail in various ways. We have devised an algorithm that fills gaps by imputing an extension of preceding live data and explain advantages. To demonstrate the effectiveness of the algorithm, we have implemented it on 512-length original datasets and have removed 30% (150 values). The resulting data is consistent with the original in all three major criteria: the noise characteristic, the distribution, and the Allan levels and slopes. Of special importance is that all Allan deviation measurements on the imputed set lie within 90% confidence of the statistic for the original dataset.
Citation
IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control

Keywords

ADEV, AVAR, clock, deviation, frequency, modified, noise, oscillator, power-law, stability, standard, time, variance.

Citation

Howe, D. and Schlossberger, N. (2022), Characterizing Frequency Stability Measurements having Multiple Data Gaps, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, [online], https://doi.org/10.1109/TUFFC.2021.3137425, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=933258 (Accessed April 17, 2024)
Created February 2, 2022, Updated March 25, 2024