Modeling and optimizing of the random atomic spin gyroscope drift based on the atomic spin gyroscope
Abstract
In order to improve the atom spin gyroscope's operational accuracy and compensate the random error caused by the nonlinear and weak-stability characteristic of the random atomic spin gyroscope (ASG) drift, the hybrid random drift error model based on autoregressive (AR) and genetic programming (GP) + genetic algorithm (GA) technique is established. The time series of random ASG drift is taken as the study object. The time series of random ASG drift is acquired by analyzing and preprocessing the measured data of ASG. The linear section model is established based on AR technique. After that, the nonlinear section model is built based on GP technique and GA is used to optimize the coefficients of the mathematic expression acquired by GP in order to obtain a more accurate model. The simulation result indicates that this hybrid model can effectively reflect the characteristics of the ASG's random drift. The square error of the ASG's random drift is reduced by 92.40%. Comparing with the AR technique and the GP + GA technique, the random drift is reduced by 9.34% and 5.06%, respectively. The hybrid modeling method can effectively compensate the ASG's random drift and improve the stability of the system.
- Authors:
-
- School of Instrument Science and Opto-Electronics Engineering, Beihang University, Beijing 100191 (China)
- Publication Date:
- OSTI Identifier:
- 22392217
- Resource Type:
- Journal Article
- Journal Name:
- Review of Scientific Instruments
- Additional Journal Information:
- Journal Volume: 85; Journal Issue: 11; Other Information: (c) 2014 AIP Publishing LLC; Country of input: International Atomic Energy Agency (IAEA); Journal ID: ISSN 0034-6748
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY; ACCURACY; ALGORITHMS; ERRORS; GYROSCOPES; NONLINEAR PROBLEMS; OPTIMIZATION; PROGRAMMING; RANDOMNESS; SIMULATION; SPIN; STABILITY
Citation Formats
Quan, Wei, Lv, Lin, and Liu, Baiqi. Modeling and optimizing of the random atomic spin gyroscope drift based on the atomic spin gyroscope. United States: N. p., 2014.
Web. doi:10.1063/1.4900946.
Quan, Wei, Lv, Lin, & Liu, Baiqi. Modeling and optimizing of the random atomic spin gyroscope drift based on the atomic spin gyroscope. United States. https://doi.org/10.1063/1.4900946
Quan, Wei, Lv, Lin, and Liu, Baiqi. 2014.
"Modeling and optimizing of the random atomic spin gyroscope drift based on the atomic spin gyroscope". United States. https://doi.org/10.1063/1.4900946.
@article{osti_22392217,
title = {Modeling and optimizing of the random atomic spin gyroscope drift based on the atomic spin gyroscope},
author = {Quan, Wei and Lv, Lin and Liu, Baiqi},
abstractNote = {In order to improve the atom spin gyroscope's operational accuracy and compensate the random error caused by the nonlinear and weak-stability characteristic of the random atomic spin gyroscope (ASG) drift, the hybrid random drift error model based on autoregressive (AR) and genetic programming (GP) + genetic algorithm (GA) technique is established. The time series of random ASG drift is taken as the study object. The time series of random ASG drift is acquired by analyzing and preprocessing the measured data of ASG. The linear section model is established based on AR technique. After that, the nonlinear section model is built based on GP technique and GA is used to optimize the coefficients of the mathematic expression acquired by GP in order to obtain a more accurate model. The simulation result indicates that this hybrid model can effectively reflect the characteristics of the ASG's random drift. The square error of the ASG's random drift is reduced by 92.40%. Comparing with the AR technique and the GP + GA technique, the random drift is reduced by 9.34% and 5.06%, respectively. The hybrid modeling method can effectively compensate the ASG's random drift and improve the stability of the system.},
doi = {10.1063/1.4900946},
url = {https://www.osti.gov/biblio/22392217},
journal = {Review of Scientific Instruments},
issn = {0034-6748},
number = 11,
volume = 85,
place = {United States},
year = {Sat Nov 15 00:00:00 EST 2014},
month = {Sat Nov 15 00:00:00 EST 2014}
}