论文信息 - Temperature compensation method using readout signals of ring laser gyroscope.

Temperature compensation method using readout signals of ring laser gyroscope.

Traditional compensation methods using temperature-related parameters have little effect when the ring laser gyroscope (RLG) bias changes rapidly. To solve this problem, a novel RLG bias temperature compensation method using readout signals is proposed in this paper. Combined with the least squares support vector machine (LS-SVM) algorithm, the novel method can improve the precision of the RLG bias. Experiments show that by utilizing the readout signals in the LS-SVM model, the RLG bias stability can be significantly raised compared to the original data. The novel method proposed in this paper is shown to be feasible, even when the RLG bias changes rapidly.

[1] Xingwu Long,et al. Optical-axis perturbation in nonplanar ring resonators , 2008 .

[2] S H Mohammadnezhad,et al. Performance Modeling of Ring Laser Gyro in Inertial Navigation System , 2006 .

[3] Guo Wei,et al. Application of Least Squares-Support Vector Machine in system-level temperature compensation of ring laser gyroscope , 2011 .

[4] Hong Woon Seon,et al. The compensation method for thermal bias of ring laser gyro , 2008, LEOS 2008 - 21st Annual Meeting of the IEEE Lasers and Electro-Optics Society.

[5] Guoyong Wu,et al. Thermal characteristics and thermal compensation of four frequency ring laser gyro , 2002, 2002 IEEE Position Location and Navigation Symposium (IEEE Cat. No.02CH37284).

[6] Chuang Guo,et al. Investigation on the temperature compensating model for ring laser gyroscope , 2006 .

[7] M. Scully,et al. The ring laser gyro , 1985 .

[8] V. Vapnik,et al. Bounds on Error Expectation for Support Vector Machines , 2000, Neural Computation.

[9] Jiancheng Fang,et al. Temperature drift modeling and compensation of RLG based on PSO tuning SVM , 2014 .

[10] Yu Wang,et al. Novel temperature modeling and compensation method for bias of ring laser gyroscope based on least-squares support vector machine , 2011 .