A Rigorous Temperature-Dependent Stochastic Modelling and Testing for MEMS-Based Inertial Sensor Errors.
A Rigorous Temperature-Dependent Stochastic Modelling and Testing for MEMS-Based Inertial Sensor Errors.
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DOI：
10.3390/s91108473
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发表时间：
2009
期刊：
影响因子：
--
通讯作者：
Pagiatakis S
中科院分区：
文献类型：
--
作者：
El-Diasty M;Pagiatakis S
本文研究了温度变化对MEMS惯性传感器随机误差的影响。我们使用安装在热室内的基于MEMS的惯性传感器来采集不同温度点下的静态数据。为了描述随机误差行为，建立了严格的随机模型，即基于自回归的高斯-马尔可夫模型。首先将所提出的基于AR的GM模型应用于短平稳惯性数据，得到随机模型参数(关联时间)。研究表明，基于MEMS的惯性器件ADIS16364的随机模型参数与温度有关。此外，利用在17℃左右采集的现场运动学测试数据，在滤波阶段使用Unscented Kalman Filter(UKF)来检验随机模型在不同温度点处的性能。结果表明，与−40°C、−20°C、0°C、+40°C和+60°C时建立的随机模型相比，在20°C下建立的随机模型提供了更精确的惯导解。该随机模型的温度相关性很大，为获得基于MEMSINS/GPS组合的最优导航解，必须始终考虑该随机模型。
In this paper, we examine the effect of changing the temperature points on MEMS-based inertial sensor random error. We collect static data under different temperature points using a MEMS-based inertial sensor mounted inside a thermal chamber. Rigorous stochastic models, namely Autoregressive-based Gauss-Markov (AR-based GM) models are developed to describe the random error behaviour. The proposed AR-based GM model is initially applied to short stationary inertial data to develop the stochastic model parameters (correlation times). It is shown that the stochastic model parameters of a MEMS-based inertial unit, namely the ADIS16364, are temperature dependent. In addition, field kinematic test data collected at about 17 °C are used to test the performance of the stochastic models at different temperature points in the filtering stage using Unscented Kalman Filter (UKF). It is shown that the stochastic model developed at 20 °C provides a more accurate inertial navigation solution than the ones obtained from the stochastic models developed at −40 °C, −20 °C, 0 °C, +40 °C, and +60 °C. The temperature dependence of the stochastic model is significant and should be considered at all times to obtain optimal navigation solution for MEMS-based INS/GPS integration.
影响因子：
2.4
作者：
El-Rabbany, A;El-Diasty, M
通讯作者：
El-Diasty, M
影响因子：
1.4
作者：
El-Diasty, Mohammed;El-Rabbany, Ahmed;Pagiatakis, Spiros
通讯作者：
Pagiatakis, Spiros
影响因子：
2.4
作者：
El-Diasty, M.;El-Rabbany, A.;Pagiatakis, S.
通讯作者：
Pagiatakis, S.