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

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.

Design and Algorithm Verification of a Gyroscope-Based Inertial Navigation System for Small-Diameter Spaces in Multilateral Horizontal Drilling Applications

“…However, it is difficult to remove the stochastic error easily by any physical method, while a precise mathematical model can solve the problem far more effectively. Although a first-order Gauss-Markov model can accurately build a stochastic model for navigation-grade INS systems, low-cost INS systems need a more precise stochastic model to ensure precision of the navigation solution because of the complex noise components existing in it [39]. The autocorrelation results ( Figure 9) from the sensor stochastic error calculations show that the errors cannot be simply modeled by a first-order Gauss-Markov model.…”

Use of MEMS Inertial Sensors for Performance Improvement of Low-cost Motion Control Systems

“…Additionally, the research on MEMS sensors has shown that the errors in their measurements are a combination of random error sources, non linearities and thermally induced alterations. A global analysis on the model of errors in MEMS accelerometers can be found in (11) , while a more detailed modeling of their measurement noise is reported in (12) , where the use of Allan's variance is clearly described. Finally, a comprehensive analysis on the bias in the measurements has been conducted in (13) .…”

Collision Vehicle Detection System Based on GPS/INS Integration

“…The calibration procedure can characterize and then delete the systematic errors from raw measurements [12] [13] [14]. To remove the systematic error, it is necessary to compare the raw data of IMU with a known reference and then determine the systematic errors which agree with that reference.…”