@inproceedings{4a1592ee92414e71aca9956d835a579a,
title = "Noise identification and analysis in MEMS sensors using an optimized variable step Allan variance",
abstract = "The Allan variance is a method of representing different noise terms imposed by the stochastic fluctuations as a function of averaging time. Unfortunately, existing implementations indicate that with the length of the analyzed datasets increasing, the computation time grows very fast. This paper presents an optimized variable step Allan variance method by changing the step length of the cluster sequence. In order to verify its validity, a series of 2-hour static data collected from Microelectromechanical Systems (MEMS) sensors are identified by the optimized algorithm and the classical one, and also applied to Dynamic Allan Variance (DAVAR) to track time-varying stability of sensors. Experiment results demonstrate that proposed algorithm could significantly speed up the estimation process of Allan variance while ensuring the accuracy of the analysis results, and enable the Allan variance becomes more efficient in practical applications.",
keywords = "Allan variance, Coefficient identification, MEMS sensor, Stochastic error",
author = "Yitong Zhang and Shuli Guo and Qiming Chen and Lina Han and Quanjin Si",
note = "Publisher Copyright: {\textcopyright} 2019 Technical Committee on Control Theory, Chinese Association of Automation.; 38th Chinese Control Conference, CCC 2019 ; Conference date: 27-07-2019 Through 30-07-2019",
year = "2019",
month = jul,
doi = "10.23919/ChiCC.2019.8865182",
language = "English",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "6309--6314",
editor = "Minyue Fu and Jian Sun",
booktitle = "Proceedings of the 38th Chinese Control Conference, CCC 2019",
address = "United States",
}