Performance evaluation of FOGs based on improved Bayes method

Mian Ye; Jinchen Zhao; Tao Wang

doi:10.1117/12.2646851

7 September 2022 Performance evaluation of FOGs based on improved Bayes method

Mian Ye, Jinchen Zhao, Tao Wang

Proceedings Volume 12329, Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022); 123293R (2022) https://doi.org/10.1117/12.2646851
Event: Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022), 2022, Changsha, China

Abstract

How to conduct FOGs (fiber optic gyros) performance evaluation under small samples condition has been the focus of FOGs research. In this paper, an improved performance evaluation method of FOGs based on modified Bayes is proposed, which fuses the random weighting method to determine the priori distribution, and combines the likelihood function established by small sample test data to evaluate the performance of FOGs. The simulation results show that the proposed method substantially reduces the length of the confidence interval without reducing the confidence, improves the accuracy of the estimation results, and has important theoretical significance and reference value for solving the FOG performance evaluation problem under small samples.

Citation Download Citation

Mian Ye, Jinchen Zhao, and Tao Wang "Performance evaluation of FOGs based on improved Bayes method", Proc. SPIE 12329, Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022), 123293R (7 September 2022); https://doi.org/10.1117/12.2646851

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available