小波分析和灰色神经网络融合的光纤陀螺误差建模与补偿

Error Modeling and Compensating of Fiber Optic Gyro Based on Wavelet Analysis and Grey Neural Network

为补偿捷联姿态测量系统中光纤陀螺因外界干扰引起的高频噪声和强漂移,提出一种基于第二代小波变换和灰色Elman神经网络融合的误差建模和补偿方法。采用Allan方差法分析了在外界干扰下的光纤陀螺输出信号,利用第二代提升小波单独重构的方法分离出陀螺误差模型中的漂移误差和白噪声,灰化漂移误差数据后建立了Elman神经网络模型并进行了补偿。实验结果表明,相较于传统的灰色理论模型和单一的Elman神经网络模型,新算法有效滤除了白噪声,并将预测模型的精度提高到96%以上,证实了模型的有效性。

In order to compensate the high frequency noises and drift errors of fiber optic gyro（FOG） in attitude measurement system under disturbing environment,a new drift error modeling method based on Ⅱ generation wavelet transform and grey neural network algorithm is proposed.The Allan variance method is adopted to analyze the output signal of FOG under disturbing circumstance.Ⅱ generation wavelet transform is applied to separate drift errors and high frequency white noises.After greying drift signal,an Elman neural network for modeling and compensation is established.Experimental results show that,compared to single grey theory model or Elman neural network,the proposed method eliminates white noise effectively,and improves modeling precision up to 96%,which increases the strike precision of combat vehicle.