基于模态分解多尺度熵的微机械陀螺仪降噪方法  

MEMS gyro scope noise reduction method based on model decomposition multi-scale entropy

作　　者：李剑 汪立新 李文华 LI Jian;WANG Lixin;LI Wenhua(College of Missile Engineering,Rocket Force Engineering University,Xi’an 710025,China)

机构地区：[1]火箭军工程大学导弹工程学院,西安710025

出　　处：《北京航空航天大学学报》2023年第10期2835-2840,共6页Journal of Beijing University of Aeronautics and Astronautics

基　　金：陕西省自然科学基础研究计划(2020JQ-491);陕西省高校科协青年人才托举计划(20200109)。

摘　　要：为了有效抑制微机械陀螺仪的随机误差,基于完备自适应噪声集合经验模态分解(CEEMDAN),结合反向传播神经网络(BPNN)建模和卡尔曼滤波(KF),提出了一种微机械陀螺仪改进的降噪方法。微机械陀螺仪数据经过CEEMDAN分解,得到本征模态分量(IMF);利用多尺度熵(MSE)算法对分量分类,对其中信号噪声混叠的分量进行反向传播神经网络辅助卡尔曼滤波;再对滤波结果和信号主导的分量进行重构,实现微机械陀螺信号降噪。实验验证了所提方法的有效性,该方法相比卡尔曼滤波、小波降噪等有更好的降噪效果。An improved MEMS gyroscope noise reduction approach is suggested based on complete ensemble EMD with adaptive noise(CEEMDAN),combined with back-propagation neural network(BPNN)modeling,and Kalman filtering(KF)method in order to effectively suppress the random error of MEMS gyroscope.The original data of the MEMS gyroscope is decomposed into the intrinsic mode function(IMF),and the IMF is classified by a multi-scale entropy(MSE)algorithm.The overlapping noise IMF is then fed back into the BPNN to assist KF,and the filter result and signal-led IMF are reconstructed to realize MEMS gyroscope signal noise reduction.Experiments show that the method has a better noise reduction effect than KF,small wave noise reduction,etc.,and improves the accuracy of the MEMS gyroscope.

关 键 词：微机械陀螺仪 经验模态分解 多尺度熵 卡尔曼滤波 神经网络 

分 类 号：V241.5[航空宇航科学与技术—飞行器设计]