基于神经网络的HRG标度因数辨识方法

惯导系统要求半球谐振陀螺仪在其大的动态范围内具有较高精度,而传统的基于最小二乘拟合的半球谐振陀螺仪标度因数辨识方法所得到的标度因数精度不高,引起较大的导航误差。为提高半球谐振陀螺仪标度因数的辨识精度,文中提出了一种基于神经网络的标度因数辨识方法,利用神经网络误差反传的梯度下降动量学习算法,对半球谐振陀螺仪的标度因数进行辨识,通过试验验证了该方法的可行性,为提高半球谐振陀螺仪工作精度,减小惯导系统导航误差提供了依据。

The Identification Method of HRG Scale Factor Based on Neural Network

The inertial navigation system has a requirement on HRG that it should have a high accuracy in the wide dynamic range,while the HRG scale factor obtained by the conventional identification method based on the least square fitting method is not very accurate thus can result in a big navigation error.To improve the accuracy of HRG scale factor,an identification method based on Neural Network is proposed in this paper.By using the gradient descending momentum learning method of neural network error back propagation,the HRG scale factor can be calibrated.The experiment has verified that this new identification method is feasible. It provides the basis for improving the HRG accuracy and descending the navigation error of inertial navigation system.