Improving Modeling of MEMS-IMUs Operating in GNSS-Denied Conditions

Yannick Stebler, Stephane Guerrier, Jan Skaloud, and Maria-Pia Victoria-Feser

Abstract:	Stochastic modeling is a challenging task for lowcost inertial sensors whose errors can have complex spectral structures. This makes the tuning process of the INS/GNSS Kalman filter often sensitive and difficult. We are currently investigating two approaches for bounding the errors in the mechanization. The first is an improved modeling of stochastic errors through the superposition of several Auto-Regressive (AR) processes. A new algorithm is presented based on the Expectation-Maximization (EM) principle that is able to estimate such complex models. The second approach focuses on redundancy through the use of multiple IMUs which don’t need to be calibrated a priori. We present a synthetic IMU computation in which the residuals are modeled by a single ARMA model. The noise power issued from the residuals is then continuously estimated by a GARCH model, which enables a proper weighting of the individual devices in the synthetic IMU.
Published in:	Proceedings of the 24th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2011) September 20 - 23, 2011 Oregon Convention Center, Portland, Oregon Portland, OR
Pages:	2417 - 2426
Cite this article:	Stebler, Yannick, Guerrier, Stephane, Skaloud, Jan, Victoria-Feser, Maria-Pia, "Improving Modeling of MEMS-IMUs Operating in GNSS-Denied Conditions," Proceedings of the 24th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2011), Portland, OR, September 2011, pp. 2417-2426.
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