传感技术学报2018，Vol.31Issue(2)：223-227,5.DOI:10.3969/j.issn.1004-1699.2018.02.012
基于卡尔曼滤波的陀螺仪降噪处理
The Noise Reduction of Gyroscope Based on Kalman Filter
张敏 1李凯 1韩焱 1史策 1李坤1
作者信息
- 1. 中北大学信息探测与处理山西省重点实验室,太原030051
- 折叠
摘要
Abstract
Based on the requirement of MEMS gyroscope to improve output accuracy and reduce random drift error,a Kalman noise reduction model based on BP neural network is built. The basic principle is introduced. Firstly,the BP neural network is used for learning to obtain accurate state equation of the system. Then,the filtering model based on BP neural network is established. Finally,the model is applied to Kalman filter to denoise the MEMS gyro signal. The semi-physical simulation experiments indicate the random walk of rate coefficient of Kalman filter data based on BP neural network model can be decreased by 6.89 times compared to the original data. This method has better noise reduction performance than the basic Kalman model and has certain application value in data processing of MEMS gyroscope.关键词
MEMS陀螺仪的随机漂移/卡尔曼滤波/BP神经网络/Allan方差辨识Key words
the random drift error of MEMS gyroscope/Kalman filter/BP neural network/Allan variance identification分类
信息技术与安全科学引用本文复制引用
张敏,李凯,韩焱,史策,李坤..基于卡尔曼滤波的陀螺仪降噪处理[J].传感技术学报,2018,31(2):223-227,5.
