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Improved Virtual Gyroscope Technology Based on the ARMA Model

机译:基于ARMA模型的改进虚拟陀螺仪技术

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摘要

In view of the large output noise and low precision of the Micro-electro-mechanical Systems (MEMS) gyroscope, the virtual gyroscope technology was used to fuse the data of the MEMS gyroscope to improve its output precision. Random error model in the conventional virtual gyroscopes contained an angular rate random walk and angle random walk ignoring other noise items and the virtual gyroscope technology can not compensate all random errors of MEMS gyroscope. So, the improved virtual gyroscope technology based on the autoregressive moving average (ARMA) model was proposed. First, the conventional virtual gyroscope technology was used to model the random error of three MEMS gyroscopes, and the data fusion was carried out by a Kalman filter to get the output of the virtual gyroscope. After that, the ARMA model was used to model the output of the virtual gyroscope, the random error model was improved with the ARMA model, and the Kalman filter was designed based on the improved random error model for data fusion of the MEMS gyroscopes. The experimental results showed that the 1σ standard deviation of the output of the virtual gyroscope based on the ARMA model was 1.4 times lower than that of the conventional virtual gyroscope output.
机译:鉴于微机电系统陀螺仪的输出噪声大,精度低,虚拟陀螺仪技术被用于融合MEMS陀螺仪的数据以提高其输出精度。传统虚拟陀螺仪中的随机误差模型包含角速率随机游走和角度随机游走,忽略了其他噪声项,并且虚拟陀螺仪技术无法补偿MEMS陀螺仪的所有随机误差。因此,提出了一种基于自回归移动平均模型的改进的虚拟陀螺仪技术。首先,使用传统的虚拟陀螺仪技术对三个MEMS陀螺仪的随机误差进行建模,并通过卡尔曼滤波器进行数据融合,以获取虚拟陀螺仪的输出。之后,使用ARMA模型对虚拟陀螺仪的输出进行建模,对随机误差模型进行了改进,并基于改进的随机误差模型设计了卡尔曼滤波器,用于MEMS陀螺仪的数据融合。实验结果表明,基于ARMA模型的虚拟陀螺仪输出的1σ标准偏差比传统的虚拟陀螺仪输出低1.4倍。

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