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Sensors 2013, 13(8), 9549-9588;

A Comparison between Different Error Modeling of MEMS Applied to GPS/INS Integrated Systems

Departament de Microelectrònica i Sistemes Electrònics, Universitat Autònoma de Barcelona (IEEC-UAB), Bellaterra, Barcelona 08193, Spain
Institut Politecnico di Torino and Istituto Superiore Mario Boella, via Pier Carlo Boggio 61, Turin 10138, Italy
Institut de Microelectr'onica de Barcelona (CNM, CSIC), UAB, Barcelona 08193, Spain
Author to whom correspondence should be addressed.
Received: 22 May 2013 / Revised: 17 July 2013 / Accepted: 20 July 2013 / Published: 24 July 2013
(This article belongs to the Special Issue Modeling, Testing and Reliability Issues in MEMS Engineering 2013)
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Advances in the development of micro-electromechanical systems (MEMS) have made possible the fabrication of cheap and small dimension accelerometers and gyroscopes, which are being used in many applications where the global positioning system (GPS) and the inertial navigation system (INS) integration is carried out, i.e., identifying track defects, terrestrial and pedestrian navigation, unmanned aerial vehicles (UAVs), stabilization of many platforms, etc. Although these MEMS sensors are low-cost, they present different errors, which degrade the accuracy of the navigation systems in a short period of time. Therefore, a suitable modeling of these errors is necessary in order to minimize them and, consequently, improve the system performance. In this work, the most used techniques currently to analyze the stochastic errors that affect these sensors are shown and compared: we examine in detail the autocorrelation, the Allan variance (AV) and the power spectral density (PSD) techniques. Subsequently, an analysis and modeling of the inertial sensors, which combines autoregressive (AR) filters and wavelet de-noising, is also achieved. Since a low-cost INS (MEMS grade) presents error sources with short-term (high-frequency) and long-term (low-frequency) components, we introduce a method that compensates for these error terms by doing a complete analysis of Allan variance, wavelet de-nosing and the selection of the level of decomposition for a suitable combination between these techniques. Eventually, in order to assess the stochastic models obtained with these techniques, the Extended Kalman Filter (EKF) of a loosely-coupled GPS/INS integration strategy is augmented with different states. Results show a comparison between the proposed method and the traditional sensor error models under GPS signal blockages using real data collected in urban roadways. View Full-Text
Keywords: Allan variance; power spectral density; INS/GPS; error modeling; MEMS; AR models; wavelet de-noising Allan variance; power spectral density; INS/GPS; error modeling; MEMS; AR models; wavelet de-noising
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Quinchia, A.G.; Falco, G.; Falletti, E.; Dovis, F.; Ferrer, C. A Comparison between Different Error Modeling of MEMS Applied to GPS/INS Integrated Systems. Sensors 2013, 13, 9549-9588.

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