A Fast, Parallel Algorithm for Fully Overlapped Allan Variance and Total Variance for Analysis and Modeling of Noise in Inertial Sensors

Modeling stochastic noise in inertial sensors-particularly those used in guidance, navigation, and control applications-involves characterizing the underlying noise process by inferring parameters such as random walks and drift rates from the Allan deviation plots. Fully overlapped Allan variance (F...

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Bibliographic Details
Published in:	IEEE sensors letters Vol. 2; no. 2; pp. 1 - 4
Main Authors:	Yadav, Shrikanth M., Shastri, Saurav Kumaraswami, Chakravarthi, Ghanashyam B., Kumar, Viraj, A., Divya Rao
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 01.06.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Allan variance (AV) Clustering algorithms Confidence Data points Deviation Drift rate Inertial sensing devices inertial sensors Instruction sets Mathematical models Matlab Noise Parallel processing Parameter estimation Process parameters Random access memory Random walk Run time (computers) sensor noise modeling and analysis Sensors stochastic errors Stochastic processes Variance analysis
ISSN:	2475-1472, 2475-1472
Online Access:	Get full text
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