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MEMS-Based Integrated Navigation.pdf
MEMS-Based Integrated Navigation
Contents
Preface
1 Microelectromechanical Systems (MEMS)
1.1 Introduction
1.2 Different Applications of MEMS Devices
1.2.1 Electric Wheelchairs
1.2.2 Personnel Tracking and Navigation
1.2.3 Agriculture
1.2.4 Event Data Recorder
1.2.5 Wildlife and Livestock Tracking
1.2.6 Patient Monitoring
1.2.7 Electronic Stability Control
1.2.8 Supplemental/Secondary Restraint System
1.2.9 Land Vehicle Navigation
1.3 Aided MEMS-Based Inertial Navigation
1.3.1 Aiding Sources in Coordinate Domain
1.3.2 Aiding Sources in Velocity Domain
1.3.3 Aiding Sources in Attitude Domain
References
2 MEMS Inertial Sensors
2.1 Introduction
2.2 Accelerometers
2.2.1 Working Principle for MEMS Accelerometers
2.2.2 Classifications of Accelerometers
2.3 Gyroscopes
2.3.1 Principle of MEMS Gyroscopes
2.3.2 Classification of MEMS Gyroscopes
2.4 MEMS Inertial Sensors for the Most Economical Land Navigation
2.5 Method to Compute Minimum Sensors
2.6 Results and Analysis
2.6.1 Drift Errors Without NHC
2.6.2 Drift Errors with NHC
References
3 MEMS Inertial Sensors Errors
3.1 Introduction
3.2 Systematic Errors
3.2.1 Bias
3.2.2 Input Sensitivity or Scale Factor
3.2.3 Nonorthogonality/ Misalignment Errors
3.2.4 Run-to-Run (Repeatability) Bias/Scale Factor
3.2.5 In Run (Stability) Bias/Scale Factor
3.2.6 Temperature-Dependent Bias/Scale Factor
3.3 Calibration of Systematic Sensor Errors
3.3.1 6-Position Static Test
3.3.2 Angular Rate Test
3.3.3 Thermal Calibration Test
3.4 Random/Stochastic Errors
3.4.1 Examples of Random Processes
3.5 Stochastic Modeling
3.5.1 Autocorrelation Function
3.5.2 Allan Variance Methodology
3.6 Sensors Measurement Models
3.6.1 Accelerometer Measurement Model
3.6.2 Gyroscope Measurement Model
References
4 Initial Alignment ofMEMS Sensors
4.1 Introduction
4.2 Considerations for MEMS Sensor Navigation
4.3 Portable Navigation System
4.4 Economical Considerations
4.4.1 Economically Desirable Configuration
4.4.2 Complete Six DOF IMU—Economically Less Desirable
4.5 Absolute Alignment
4.5.1 Static Alignment for MEMS Sensors
4.5.2 Static Alignment Example
4.6 Velocity Matching Alignment
4.6.1 GPS Derived Heading Example
4.7 Transfer Alignment
References
5 Navigation Equations
5.1 Introduction—Mathematical Relations and Transformations Between Frames
5.1.1 e-Frame to i-Frame
5.1.2 ENU l-Frame to e-Frame
5.1.3 NED l-Frame to e-Frame
5.1.4 b-Frame to ENU l-Frame
5.1.5 b-Frame to NED l-Frame
5.2 Motion Modeling in the l-Frame
5.2.1 ENU Realization
5.2.2 NED Realization
5.3 Solving Mechanization Equations
5.3.1 Classical Method
5.3.2 Half-Interval Method
References
6 Aiding MEMS-Based INS
6.1 Introduction
6.1.1 Loosely Coupled Mode of Integration
6.1.2 Tightly Coupled Mode of Integration
6.2 Introduction to Kalman Filter
6.2.1 Dynamic Model
6.2.2 Measurement Model
6.3 Kalman Filter Algorithm
6.3.1 The Prediction Stage
6.3.2 The Update Stage
6.4 Introduction to Extended Kalman Filter
6.4.1 Linearization
6.4.2 EKF Limitations
References
7 Artificial Neural Networks
7.1 Introduction
7.2 Types of ANNs
7.2.1 Multilayer Perception Neural Network (MLPNN)
7.2.2 Radial Basis Function Neural Network (RBFNN)
7.2.3 Adaptive Neuro Fuzzy Inference System (ANFIS)
7.3 Whole Navigation States Architecture
7.3.1 Example of Position Update Architecture
7.3.2 Example of Position and Velocity Update Architecture
7.4 Navigation Error States Architecture
7.4.1 Architecture for INS/GPS Integration
7.4.2 System Implementation
7.4.3 Combined P – dP and V – dV Architecture for INS/GPS
7.4.4 ANN/KF Augmented Module for INS/GPS Integration
References
8 Particle Filters
8.1 Introduction
8.2 The Monte Carlo Principle
8.3 Importance Sampling Method
8.4 Resampling Methods
8.4.1 Simple Random Resampling
8.4.2 Systematic Resampling (SR)
8.4.3 Stratified Resampling
8.4.4 Residual Resampling
8.5 Basic Particle Filters
8.6 Types of Particle Filters
8.6.1 Extended Particle Filter (EPF) and Unscented Particle Filter (UPF)
8.6.2 Rao-Blackwellized Particle Filter (RBPF)
8.6.3 Likelihood Particle Filter (LPF)
8.6.4 Regularized Particle Filter (RPF)
8.6.5 Gaussian Particle Filter (GPF) and Gaussian Sum Particle Filter
8.7 Hybrid Extended Particle Filter (HEPF)
8.7.1 Zero Velocity Condition Detection Algorithm
8.7.2 Algorithm of the Hybrid Extended Particle Filter
8.7.3 HEPF Results
8.7.4 Partial Sensor Configuration
References
Appendix A Linearization Process for the EKF for Low-Cost Navigation
A.1 System Model for Loosely Coupled Approach
A.1.1 Attitude Errors
A.1.2 Velocity Linearization
A.1.3 Position Linearization
A.1.4 Sensor Errors
A.2 GPS Measurement Model
A.3 System Model for the Tightly Coupled Approach
A.4 The Update Stage
A.5 Pseudorange and Doppler Corrections
References
About the Authors
Index
MEMS-Based
Integrated Navigation
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For a listing of recent titles in the Artech House
GNSS Technology and Application Series,
please turn to the back of this book.
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MEMS-Based
Integrated Navigation
Priyanka Aggarwal
Zainab Syed
Aboelmagd Noureldin
Naser El-Sheimy
a r t e c h h o u s e . c o m
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Library of Congress Cataloging-in-Publication Data
A catalog record for this book is available from the U.S. Library of Congress.
British Library Cataloguing in Publication Data
A catalogue record for this book is available from the British Library.
ISBN-13: 978-1-60807-043-5
Cover design by Vicki Kane
© 2010 ArteCh house
685 Canton street
Norwood, MA 02062
All rights reserved. Printed and bound in the United States of America. No part of this book
may be reproduced or utilized in any form or by any means, electronic or mechanical, including
photocopying, recording, or by any information storage and retrieval system, without permis-
sion in writing from the publisher.
All terms mentioned in this book that are known to be trademarks or service marks have
been appropriately capitalized. Artech House cannot attest to the accuracy of this information.
Use of a term in this book should not be regarded as affecting the validity of any trademark
or service mark.
10 9 8 7 6 5 4 3 2 1
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Contents
1
Preface
Microelectromechanical Systems (MEMS)
Introduction
1.1
1.2 Different Applications of MEMS Devices
1.2.1 Electric Wheelchairs
1.2.2 Personnel Tracking and Navigation
1.2.3 Agriculture
1.2.4 Event Data Recorder
1.2.5 Wildlife and Livestock Tracking
1.2.6 Patient Monitoring
1.2.7 Electronic Stability Control
1.2.8 Supplemental/Secondary Restraint System
1.2.9 Land Vehicle Navigation
1.3 Aided MEMS-Based Inertial Navigation
1.3.1 Aiding Sources in Coordinate Domain
1.3.2 Aiding Sources in Velocity Domain
1.3.3 Aiding Sources in Attitude Domain
References
2
MEMS Inertial Sensors
Introduction
2.1
2.2 Accelerometers
2.2.1 Working Principle for MEMS Accelerometers
2.2.2 Classifications of Accelerometers
v
xi
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vi
MEMS-Based Integrated Navigation
2.3 Gyroscopes
2.3.1 Principle of MEMS Gyroscopes
2.3.2 Classification of MEMS Gyroscopes
2.4 MEMS Inertial Sensors for the Most Economical
Land Navigation
2.5 Method to Compute Minimum Sensors
2.6 Results and Analysis
2.6.1 Drift Errors Without NHC
2.6.2 Drift Errors with NHC
References
3
MEMS Inertial Sensors Errors
3.1
Introduction
3.2 Systematic Errors
3.2.1 Bias
3.2.2 Input Sensitivity or Scale Factor
3.2.3 Nonorthogonality/Misalignment Errors
3.2.4 Run-to-Run (Repeatability) Bias/Scale Factor
3.2.5 In Run (Stability) Bias/Scale Factor
3.2.6 Temperature-Dependent Bias/Scale Factor
3.3 Calibration of Systematic Sensor Errors
3.3.1 6-Position Static Test
3.3.2 Angular Rate Test
3.3.3 Thermal Calibration Test
3.4 Random/Stochastic Errors
3.4.1 Examples of Random Processes
3.5 Stochastic Modeling
3.5.1 Autocorrelation Function
3.5.2 Allan Variance Methodology
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Contents
3.6 Sensors Measurement Models
3.6.1 Accelerometer Measurement Model
3.6.2 Gyroscope Measurement Model
4
References
Initial Alignment of MEMS Sensors
4.1
Introduction
4.2 Considerations for MEMS Sensor Navigation
4.3 Portable Navigation System
vii
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4.4 Economical Considerations
4.4.1 Economically Desirable Configuration
68
4.4.2 Complete Six DOF IMU—Economically Less Desirable 74
4.5 Absolute Alignment
4.5.1 Static Alignment for MEMS Sensors
4.5.2 Static Alignment Example
4.6 Velocity Matching Alignment
4.6.1 GPS Derived Heading Example
4.7 Transfer Alignment
References
5
Navigation Equations
5.1
Introduction—Mathematical Relations and
Transformations Between Frames
5.1.1 e-Frame to i-Frame
5.1.2 ENU l-Frame to e-Frame
5.1.3 NED l-Frame to e-Frame
5.1.4 b-Frame to ENU l-Frame
5.1.5 b-Frame to NED l-Frame
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