TY - GEN
T1 - A method to analyse and eliminate stochastic noises of FOG based on ARMA and kalman filtering method
AU - Li, Xiaojing
AU - Chen, Jiabin
AU - Shangguan, Yong
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/9/25
Y1 - 2014/9/25
N2 - This paper presents a method to model and analyze stochastic noises of fiber-optic gyroscope (FOG) in a strapdown inertial navigation system (SINS). Auto-regressive and moving average (ARMA) model was constructed using the method of time series. AR(3) model was best suited according to Akaike Information Criterion, while ARMA(2, 1) model was more often used in general engineering practise. In order to compare the difference of the two models, Kalman filtering algorithms were constructed specifically according to parameters of the two models. Amplitudes and Allan variances of the five FOG stochastic noises were calculated and compared to validate the effectiveness of Kalman filtering method. Simulation results show that Kalman filtering method can effectively eliminate stochastic noises of FOG, but that parameters of Kalman filtering which are related with the type of ARMA model should be regulated specifically.
AB - This paper presents a method to model and analyze stochastic noises of fiber-optic gyroscope (FOG) in a strapdown inertial navigation system (SINS). Auto-regressive and moving average (ARMA) model was constructed using the method of time series. AR(3) model was best suited according to Akaike Information Criterion, while ARMA(2, 1) model was more often used in general engineering practise. In order to compare the difference of the two models, Kalman filtering algorithms were constructed specifically according to parameters of the two models. Amplitudes and Allan variances of the five FOG stochastic noises were calculated and compared to validate the effectiveness of Kalman filtering method. Simulation results show that Kalman filtering method can effectively eliminate stochastic noises of FOG, but that parameters of Kalman filtering which are related with the type of ARMA model should be regulated specifically.
KW - Allan variance
KW - Kalman filtering method
KW - auto-regressive and moving average model
KW - fiber-optic gyroscope
KW - stochastic noises
UR - http://www.scopus.com/inward/record.url?scp=84908884939&partnerID=8YFLogxK
U2 - 10.1109/IHMSC.2014.180
DO - 10.1109/IHMSC.2014.180
M3 - Conference contribution
AN - SCOPUS:84908884939
T3 - Proceedings - 2014 6th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2014
SP - 325
EP - 328
BT - Proceedings - 2014 6th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 6th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2014
Y2 - 26 August 2014 through 27 August 2014
ER -
