...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Micromachines >Application of Improved Wavelet Thresholding Method and an RBF Network in the Error Compensating of an MEMS Gyroscope
【24h】

Application of Improved Wavelet Thresholding Method and an RBF Network in the Error Compensating of an MEMS Gyroscope

机译：改进的小波阈值法和RBF网络在MEMS陀螺仪误差补偿中的应用

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The large random errors in Micro-Electro-Mechanical System (MEMS) gyros are one of the major factors that affect the precision of inertial navigation systems. Based on the indoor inertial navigation system, an improved wavelet threshold de-noising method was proposed and combined with a gradient radial basis function (RBF) neural network to better compensate errors. We analyzed the random errors in an MEMS gyroscope by using Allan variance, and introduced the traditional wavelet threshold methods. Then, we improved the methods and proposed a new threshold function. The new method can be used more effectively to detach white noise and drift error in the error model. Finally, the drift data was modeled and analyzed in combination with the RBF neural network. Experimental results indicate that the method is effective, and this is of great significance for improving the accuracy of indoor inertial navigation based on MEMS gyroscopes.
机译：微机电系统（MEMS）陀螺仪中较大的随机误差是影响惯性导航系统精度的主要因素之一。在室内惯性导航系统的基础上，提出了一种改进的小波阈值去噪方法，并结合了梯度径向基函数神经网络，较好地补偿了误差。我们利用Allan方差分析了MEMS陀螺仪中的随机误差，并介绍了传统的小波阈值方法。然后，我们改进了方法并提出了新的阈值函数。该新方法可以更有效地用于消除误差模型中的白噪声和漂移误差。最后，结合RBF神经网络对漂移数据进行建模和分析。实验结果表明，该方法是有效的，对于提高基于MEMS陀螺仪的室内惯性导航的精度具有重要意义。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号