Wang Guochen, Wang Qiuying, Zhao Bo, Wang Zhenpeng
Appl Opt. 2016 Feb 10;55(5):1061-6. doi: 10.1364/AO.55.001061.
Aiming to improve the bias stability of the fiber optical gyroscope (FOG) in an ambient temperature-change environment, a temperature-compensation method based on the relevance vector machine (RVM) under Bayesian framework is proposed and applied. Compared with other temperature models such as quadratic polynomial regression, neural network, and the support vector machine, the proposed RVM method possesses higher accuracy to explain the temperature dependence of the FOG gyro bias. Experimental results indicate that, with the proposed RVM method, the bias stability of an FOG can be apparently reduced in the whole temperature ranging from -40°C to 60°C. Therefore, the proposed method can effectively improve the adaptability of the FOG in a changing temperature environment.
为了提高光纤陀螺仪（FOG）在环境温度变化环境中的偏置稳定性，提出并应用了一种基于贝叶斯框架下相关向量机（RVM）的温度补偿方法。与二次多项式回归、神经网络和支持向量机等其他温度模型相比，所提出的RVM方法在解释FOG陀螺偏置的温度依赖性方面具有更高的精度。实验结果表明，采用所提出的RVM方法，在-40°C至60°C的整个温度范围内，FOG的偏置稳定性可明显降低。因此，该方法能有效提高FOG在温度变化环境中的适应性。
