基于小波神经网络的光纤陀螺系统级温度补偿

捷联惯组中光纤陀螺的输出精度受温度的影响较大，在实际应用中必须对其进行温度漂移补偿。对于光纤陀螺的零偏随温度变化呈较强的非线性特性，传统的多元线性回归法难以满足补偿精度要求，因而将小波神经网络用于建立光纤陀螺的温度补偿模型。通过对某型号捷联惯组中光纤陀螺的静漂数据进行仿真，实验结果表明，基于小波神经网络模型比多元线性回归模型的补偿效果更明显，有效提高了陀螺的精度。

System level Temperature Compensation of FOG Based on Wavelet Neural Network

The output accuracy of fiber optic gyroscope (FOG) in the strapdown inertial measuring unit (SIMU) is significantly influenced by temperature, and the temperature drift compensation must be carried out in practical applications. The bias drift in FOG has shown strong non linear characteristics in terms of the change of the temperature, the traditional multivariate linear regression method is hard to meet the requirement of compensation accuracy. Therefore, the wavelet neural network is applied here to establish the temperature compensation model of FOG. By simulating the static drift data of FOG for certain model of SIMU, the experimental results show that the compensation effect based on the wavelet neural network model is better than the traditional multivariate model, and the precision of the FOG is improved effectively.