Combining numerous gyroscopes for accuracy improvement using autoregressive process for rate signal modeling

Qiang Shen, Jieyu Liu, W. Qin, Huang Huang
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引用次数: 1

Abstract

A signal processing technique for gyro array is presented in this paper to reduce noise and improve the accuracy of micro-electro-mechanical system (MEMS) gyroscope. To improve the dynamic performance, the true rate is modeled by an autoregressive (AR) model instead of a random walk. According to the analysis results of the Allan variance, the measurement model is simplified and a mathematical model is established. Based on this model, a novel Kalman filter (KF) with modified estimation process for combining outputs of a gyroscope array is designed. The performance and the affect factors of the gyro array are analyzed by using a steady-state covariance. The experimental results indicate that the RMSE of the gyroscopes can be reduced from 0.4925 deg/s to 0.0034 deg/s by an array composed of six gyroscopes in static test. The dynamic test is also discussed and the validity of the proposed modeling and fusion method is proved.
结合多个陀螺仪,利用自回归过程进行速率信号建模,提高精度
为了降低噪声,提高微机电系统(MEMS)陀螺仪的精度,提出了一种陀螺仪阵列信号处理技术。为了提高系统的动态性能,采用自回归(AR)模型代替随机漫步模型。根据Allan方差的分析结果,对测量模型进行了简化,建立了数学模型。基于该模型,设计了一种具有改进估计过程的新型卡尔曼滤波器,用于组合陀螺仪阵列输出。利用稳态协方差分析了陀螺阵列的性能及其影响因素。实验结果表明,在静态测试中,由6个陀螺仪组成的阵列可以将陀螺仪的均方根误差从0.4925°/s降低到0.0034°/s。最后进行了动态试验,验证了所提出的建模和融合方法的有效性。
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