Temperature variation effects on stochastic characteristics for low-cost MEMS-based inertial sensor error
Temperature variation effects on stochastic characteristics for low-cost MEMS-based inertial sensor error
复制标题
DOI：
10.1088/0957-0233/18/11/009
复制
发表时间：
2007-11-01
影响因子：
2.4
通讯作者：
Pagiatakis, S.
中科院分区：
文献类型：
--
作者：
El-Diasty, M.;El-Rabbany, A.;Pagiatakis, S.
我们使用Allan方差和最小二乘谱分析（LSSA）研究了不同温度点对MEMS惯性传感器噪声模型的影响。Allan方差是一种将均方根随机漂移误差表示为平均时间的函数的方法。LSSA是经典傅立叶方法的一种替代方法，已被许多研究人员成功地应用于实验序列噪声特性的研究。使用两个基于MEMS的伊穆斯（MotionPakII和Crossbow AHRS 300 CC）在不同温度点收集静态数据集。根据不同温度点的Allan方差估计结果预测了两种MEMS惯性传感器的性能，并利用LSSA研究了噪声特性，确定了传感器的随机模型参数。研究表明，利用Allan方差估计和LSSA可以辨识MEMS惯性传感器的随机特性，且传感器的随机模型参数与温度有关。此外，还采用凯撒窗FIR低通滤波器研究了去噪阶段对随机模型的影响。结果表明，随机模型也依赖于所选择的截止频率。
We examine the effect of varying the temperature points on MEMS inertial sensors' noise models using Allan variance and least-squares spectral analysis (LSSA). Allan variance is a method of representing root-mean-square random drift error as a function of averaging times. LSSA is an alternative to the classical Fourier methods and has been applied successfully by a number of researchers in the study of the noise characteristics of experimental series. Static data sets are collected at different temperature points using two MEMS-based IMUs, namely MotionPakII and Crossbow AHRS300CC. The performance of the two MEMS inertial sensors is predicted from the Allan variance estimation results at different temperature points and the LSSA is used to study the noise characteristics and define the sensors' stochastic model parameters. It is shown that the stochastic characteristics of MEMS-based inertial sensors can be identified using Allan variance estimation and LSSA and the sensors' stochastic model parameters are temperature dependent. Also, the Kaiser window FIR low-pass filter is used to investigate the effect of de-noising stage on the stochastic model. It is shown that the stochastic model is also dependent on the chosen cut-off frequency.