ON IMPROVING THE ACCURACY AND RELIABILITY OF GPS/INS-BASED DIRECT SENSOR ...

237 Pages·2007·2.08 MB·English

ON IMPROVING THE ACCURACY AND RELIABILITY
OF GPS/INS-BASED DIRECT SENSOR GEOREFERENCING

DISSERTATION
Presented in Partial Fulfillment of the Requirements
for the Degree Doctor of Philosophy in the Graduate School
of The Ohio State University
by
Yudan Yi, M.S.
*****
The Ohio State University
2007

Dissertation Committee: Approved by
Prof. Dorota A. Grejner-Brzezinska, Adviser
Prof. Burkhard Schaffrin __________________________________________
Prof. Toni Schenk Adviser
Dr. Charles Toth Geodetic Science and Surveying Graduate Program
ABSTRACT

Due to the complementary error characteristics of the Global Positioning System
(GPS) and Inertial Navigation System (INS), their integration has become a core
positioning component, providing high-accuracy direct sensor georeferencing for
multi-sensor mobile mapping systems. Despite significant progress over the last decade,
there is still a room for improvements of the georeferencing performance using
specialized algorithmic approaches. The techniques considered in this dissertation include:
(1) improved single-epoch GPS positioning method supporting network mode, as
compared to the traditional real-time kinematic techniques using on-the-fly ambiguity
resolution in a single-baseline mode; (2) customized random error modeling of inertial
sensors; (3) wavelet-based signal denoising, specially for low-accuracy high-noise
Micro-Electro-Mechanical Systems (MEMS) inertial sensors; (4) nonlinear filters,
namely the Unscented Kalman Filter (UKF) and the Particle Filter (PF), proposed as
alternatives to the commonly used traditional Extended Kalman Filter (EKF).
The network-based single-epoch positioning technique offers a better way to
calibrate the inertial sensor, and then to achieve a fast, reliable and accurate navigation
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solution. Such an implementation provides a centimeter-level positioning accuracy
independently on the baseline length. The advanced sensor error identification using the
Allan Variance and Power Spectral Density (PSD) methods, combined with a
wavelet-based signal de-noising technique, assures reliable and better description of the
error characteristics, customized for each inertial sensor. These, in turn, lead to a more
reliable and consistent position and orientation accuracy, even for the low-cost inertial
sensors. With the aid of the wavelet de-noising technique and the customized error model,
around 30 percent positioning accuracy improvement can be found, as compared to the
solution using raw inertial measurements with the default manufacturer’s error models.
The alternative filters, UKF and PF, provide more advanced data fusion techniques and
allow the tolerance of larger initial alignment errors. They handle the unknown nonlinear
dynamics better, in comparison to EKF, resulting in a more reliable and accurate
integrated system. For the high-end inertial sensors, they provide only a slightly better
performance in terms of the tolerance to the losses of GPS lock and orientation
convergence speed, whereas the performance improvements are more pronounced for the
low-cost inertial sensors.

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Dedicated to my family

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ACKNOWLEDGMENTS
First and foremost, I would like to thank my advisor, Dr. Dorota A.
Grejner-Brzezinska, for her continuous guidance and assistance throughout my Ph.D.
studies and research. Without her academic and financial support, this dissertation could
not have been done. My special thanks go to Dr. Charles Toth who gave me helpful
suggestions in my research. Charles is a good adviser as well as a good friend. He
encouraged me and helped me when I was frustrated and when I doubted in myself. I
thank him for his constant support and inspiration for all these years.
I would like to express my warm and sincere thanks to Dr. Burkhard Schaffrin who
has generously given his time and expertise to review my research papers, and provided
detailed and valuable comments on my dissertation. I would also like to thank Dr. Clyde
Goad for his understanding and kind support during the time I worked on this dissertation.
My thanks also go to Dr. Toni Schenk, Dr. C. K. Shum, Dr. Chris Jekeli, Mrs. Irene
Tesfai and countless others for their comments and help in my studies and research.
Last, but not least, I would like to thank my parents, Feng Yi and Yan Liu, for giving
me life, taking care of me, and supporting my education. I am especially grateful to my
wife, Yan Lin, for being there for me whether in good circumstances or through difficult
times.
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VITA
June 29, 1974………........ Born in Yichun, Jiangxin, P.R.China
July 1995………………... B.S., Satellite Geodesy, Wuhan Technical University of
Surveying and Mapping, Wuhan, P.R.China
May 1998……………….. M.S., Satellite Geodesy, Tongji University, Shanghai,
P.R.China
March 1998-Aug. 2000…. Engineer, Geographical Information System Center at
Zhongshan, Guangdong, P.R.China
May 2003……………….. M.S., Satellite Geodesy, The Ohio State University,
Columbus, OH, USA
Aug. 2000-Dec. 2005…… Graduate Research Associate, The Ohio State University,
Columbus, OH, USA
Jan. 2006-present……….. Geodesist, Topcon Positioning Systems Inc., Columbus,
OH, USA

PUBLICATIONS
Research Publication
1. Grejner-Brzezinska D. A., Y Yi and C. K. Toth (2001). Precision GPS/INS Navigation
in Urban Canyons: Dealing with GPS Losses of Lock, Proc. of IAG 2001 Scientific
Assembly, Budapest, Hungary, CD ROM.
2. Grejner-Brzezinska, D.A., Y. Yi, C. K. Toth (2002). Bridging GPS Gaps in Urban
Canyons: The Benefits of ZUPT, Navigation, Vol. 48, No. 4, pp. 217–225.
3. Grejner-Brzezinska D. A., Y. Yi and J. Wang (2002). Design and Navigation
Performance Analysis of an Experimental GPS/INS/PL System, Proc. of 2nd
Symposium on Geodesy for Geotechnical and Structural Engineering, Berlin,
Germany, pp. 452-461.
4. Grejner-Brzezinska D., Y. Yi, R. Salman, D. Kopcha, R. Anderson, J. Davenport and J.
Graham (2003). Enhanced Gravity Compensation for Improved Inertial Navigation
Accuracy, Proc. of ION GPS-2003, Portland, Oregon, CD-ROM, pp. 2897–2909.
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5. Grejner-Brzezinska, D. A. and Y. Yi (2003). Experimental GPS/INS/Pseudolite
System for Kinematic Positioning, Survey Review, Vol. 37, No. 288.
6. Grejner-Brzezinska, D.A., Y. Yi, C. K. Toth, R. Anderson, J. Davenport, D. Kopcha,
R. Salman (2004). On the Improved Gravity Compensation in Inertial Navigation,
Photogrammetric Engineering & Remote Sensing, Vol. 70, No. 6, pp. 663-664
7. Grejner-Brzezinska, D. A., C. K. Toth and Y. Yi, (2005). On Improving Navigation
Accuracy of GPS/INS Systems, Photogrammetirc Engineering & Remote Sensing,
Vol. 71, No. 4, pp 377-389.
8. Grejner-Brzezinska, D. A., C. K. Toth and Y. Yi (2005). The Assessment of the
Impact of Stochastic Error Modeling, Signal Denoising and Improved Gravity
Compensation on the NavigationPerformance of the Multi-IMU/GPS Sensor
Assembly, Proc. of ION AM-2005, Cambridge, Massachusetts, 2005, CD-ROM, pp.
988–999.
9. Yi Y. (2002). Robust GPS/INS Integrated System in Urban Region:
GPS/INS/Pseudolite Integration, Proc. of ION GPS-2002, Portland, Oregon,
CD-ROM, pp. 2396-2405.
10. Yi Y. (2002). Performance Analysis of Land-Based GPS/INS/Pseudolite Integrated
System, Proc. of Heiskanen Symposium in Geodesy, Columbus, OH, CD-ROM.
11. Yi Y. and D. A. Grejner-Brzezinska (2003). Kinematic Carrier Phase GPS Positioning
Aided by an Instantaneous Local Ionospheric Model Based on Multiple Base Stations
Using Kriging, Proc. of ION AM-2003, Albuquerque, NM, CD-ROM, pp. 387-396.
12. Yi Y., D. A. Grejner-Brzezinska, C. K. Toth, J. Wang and C. Rizos (2003). GPS + INS
+ Pseudolites, Innovation column of GPS World 14(7): 42-46, 48-49
13. Yi Y. and D. A. Grejner-Brzezinska (2004). Kinematic Carrier Phase Positioning
Based on Multiple Base and Rover Receivers, Proc. of the 4th International
Symposium of Mobile Mapping Technology (MMT 2004), Kunming, China,
CD-ROM.
14. Yi Y., D. A. Grejner-Brzezinska and C. K. Toth (2005). Performance Analysis of a
Low Cost MEMS IMU and GPS Integration, Proc. of ION AM-2005, Cambridge,
Massachusetts, CD-ROM, pp. 1026–1036.
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15. Yi Y. and D. A. Grejner-Brzezinska (2005). Nonlinear Bayesian filters: Alternatives
to Extended Kalman Filter in GPS/INS fusion systems, Proc. of ION GNSS-2005,
Long Beach, CA, CD ROM, pp. 1391–1400.
16. Yi Y. and D. A. Grejner-Brzezinska (2006). Performance Comparison of the
Nonlinear Bayesian Filters Supporting GPS/INS Integration, Proc. of ION
NTM-2006, Monterey, CA, CD ROM, pp. 977–983.
17. Yi Y. and D. A. Grejner-Brzezinska (2006). Tightly-coupled GPS/INS Integration
Using Unscented Kalman Filter and Particle Filter, Proc. of ION GPS-2006, Fort
Worth, TX, CD ROM, pp. 2182–2191.
18. Yi Y. (2007). On Improving the Accuracy and Reliability of GPS/INS-based Direct
Sensor Georeferencing, Geodetic Science and Surveying Report number 484
(http://www.ceegs.ohio-state.edu/gsreports/), The Ohio State University, Columbus,
OH.

FIELDS OF STUDY

Major Field: Geodetic Science and Surveying

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TABLE OF CONTENTS

Pages
Abstract…………………………………………………………………………… ii
Dedication………………………………………………………………………… iv
Acknowledgments………………………………………………………………… v
Vita………………………………………………………………………………... vi
List of Abbreviations……………………………………………………………... xii
List of Tables……………………………………………………………………... xv
List of Figures…………………………………………………………………….. xviii

Chapters:

1 Introduction…………………………………………………………………... 1
1.1 Background and motivation……………………………………………. 1
1.2 Structure of the dissertation……………………………………………. 11

2 GPS/INS integration…………………………………………………………. 13
2.1 Introduction to the Global Positioning System………………………… 13
2.1.1 One-way GPS measurement model……………………………….. 14
2.1.2 Double-difference GPS measurement model……………………... 19
2.1.3 GPS velocity and orientation determination………………………. 21
2.2 Introduction to the Inertial Navigation System………………………... 22
2.2.1 INS principle……………………………………………………… 22
2.2.2 INS error characteristics………………………………………….. 26
2.2.3 INS initialization and initial alignment…………………………… 31
2.3 Introduction to GPS/INS integration………………………………….. 32
2.3.1 GPS/INS integration mode……………………………………….. 32
2.3.2 Linear filtering – Kalman Filter…………………………………... 37
2.3.3 Nonlinear filtering………………………………………………… 41
2.3.3.1 Extended Kalman Filter…………………………………….. 41
2.3.3.2 Other nonlinear filters………………………………………. 43
2.4 OSU GPS/INS AIMSTM system……………………………………….. 45
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3 Instantaneous positioning technique support network mode………………… 54
3.1 Motivation……………………………………………………………... 54
3.2 Real-time kinematic instantaneous GPS positioning approach………... 59
3.2.1 Double-difference GPS positioning model with ionospheric
pseudo-observations………………………………………………. 59
3.2.2 Single-epoch GPS ambiguity resolution………………………….. 64
3.2.3 Stochastic model based on variance component model…………... 69
3.2.4 Partial integer ambiguity fixing strategy………………………….. 73
3.2.5 Integer ambiguity bridging between consecutive epochs…………. 73
3.3 Analysis of experimental static data…………………………………... 75
3.3.1 Baseline-by-baseline mode………………………………………... 75
3.3.2 Network mode…………………………………………………….. 82
3.4 Analysis of experimental kinematic data………………………………. 84

4 Extended stochastic inertial-sensor error identification and modeling………. 94
4.1 Primary INS error sources……………………………………………... 95
4.2 INS dynamic error modeling…………………………………………... 100
4.3 INS stochastic error identification and modeling……………………… 101
4.3.1 INS random error identification…………………………………... 103
4.3.1.1 Allan variance analysis……………………………………... 103
4.3.1.2 Power spectral density (PSD) method ……………………... 107
4.3.1.3 Numerical examples………………………………………… 110
4.3.1.4 Summary of the INS stochastic error identification using the
Allan variance analysis and the PSD method………………. 119
4.3.2 INS random error modeling……………………………………….. 122
4.3.3 Performance comparisons of the customized error model as
compared to the default error model from manufacturer’s error
specifications……………………………………………………… 125

5 Wavelet-based signal de-noising technique………………………………….. 127
5.1 Wavelet signal de-noising technique for a low cost MEMS IMU…….. 127
5.2 The effects of the wavelet-based de-noising on the initial static coarse
alignment………………………………………………………………. 137
5.3 The effects of the wavelet-based de-noising on the kinematic
navigation solutions……………………………………………………. 139
5.4 The effects of the wavelet de-noising, combined with the customized
stochastic error model on kinematic navigation solutions……………... 141

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