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Department of Earth and Space Science and Engineering, York University, Toronto, ON M3J 1P3, Canada





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Abstract 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. View Full-Text

Keywords: MEMS; inertial sensor; temperature; AR model; GM model; UKF MEMS; inertial sensor; temperature; AR model; GM model; UKF





Autor: Mohammed El-Diasty * and Spiros Pagiatakis

Fuente: http://mdpi.com/



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