Next Article in Journal
An Advanced Temporal Credential-Based Security Scheme with Mutual Authentication and Key Agreement for Wireless Sensor Networks
Next Article in Special Issue
Bandwidth Optimization Design of a Multi Degree of Freedom MEMS Gyroscope
Previous Article in Journal
Correction: Ferreira, P.M., et al. A Neural Network Based Intelligent Predictive Sensor for Cloudiness, Solar Radiation and Air Temperature. Sensors 2012, 12, 15750–15777
Previous Article in Special Issue
A Radiosonde Using a Humidity Sensor Array with a Platinum Resistance Heater and Multi-Sensor Data Fusion
Sensors 2013, 13(8), 9549-9588; doi:10.3390/s130809549
Article

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

1,* , 2
,
2
,
2
 and
1,3
1 Departament de Microelectrònica i Sistemes Electrònics, Universitat Autònoma de Barcelona (IEEC-UAB), Bellaterra, Barcelona 08193, Spain 2 Institut Politecnico di Torino and Istituto Superiore Mario Boella, via Pier Carlo Boggio 61, Turin 10138, Italy 3 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)

Abstract

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.
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 which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Share & Cite This Article

Further Mendeley | CiteULike
Export to BibTeX |
EndNote
MDPI and ACS Style

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.

View more citation formats

Related Articles

Article Metrics

For more information on the journal, click here

Comments

Cited By

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert