神经网络拟合的激光陀螺惯组重力实时测量滤波
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国防科技大学

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V241.5+58

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国家自然科学基金资助项目(62203454)


Real time gravity measurement filtering of laser gyro inertial group fitted by neural network
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    摘要:

    为了解决激光陀螺惯组重力测量系统中传统重力滤波方法的固定长时延问题,提出了一种基于神经网络拟合滤波的重力数据实时处理方法,通过对有限长单位冲激响应(finite impulse response,FIR)滤波器进行神经网络拟合,降低对未来数据的长依赖,从而有效减少滤波时延。实验结果表明,与FIR滤波器相比,该方法的处理时延降低了93%,平均滤波精度优于2mGal,表明所提方法在保持较高精度的同时显著提升了数据处理的实时性,为激光陀螺惯组重力测量系统的实时重力测量提供了解决方案。

    Abstract:

    To overcome the inherent fixed time-delay limitation of conventional gravity filtering methods in laser gyro-based inertial navigation gravity measurement systems, this paper proposes a real-time gravity data processing method based on neural network-approximated FIR (finite impulse response) filtering. By fitting the FIR filter through neural network implementation, the long dependency on future data was effectively reduced, thereby effectively reducing filtering delay. Experimental results show that compared with the FIR filter, the processing delay of the proposed method is reduced by 93%, and the average filtering accuracy is better than 2mGal. This indicates that the proposed method can significantly improve the real-time performance of data processing while maintaining high accuracy, providing a solution for the real-time gravity measurement of the laser gyroscope inertial group gravity measurement system.

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历史
  • 收稿日期:2025-01-13
  • 最后修改日期:2025-07-04
  • 录用日期:2025-04-28
  • 在线发布日期: 2025-07-08
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