Pedestrian Inertial Navigation with Self-Contained Aiding (IEEE Press Series

YUSHENG WANG, PhD, received the B.Eng. degree (Hons.) in engineering mechanics from Tsinghua University, Beijing, China, in 2014 and the Ph.D. degree in mechanical and aerospace engineering from the University of California, Irvine, CA, in 2020. His research interests include the development of silicon-based and fused quartz-based MEMS resonators and gyroscopes, and pedestrian inertial navigation development with sensor fusion. He is currently working at SiTime Corporation as an MEMS Development Engineer. ANDREI M. SHKEL, PhD, has been on faculty at the University of California, Irvine since 2000, and served as a Program Manager in the Microsystems Technology Office of DARPA. His research interests are reflected in over 300 publications, 42 patents, and 3 books. Dr. Shkel has been on a number of editorial boards, including Editor of IEEE/ASME JMEMS, Journal of Gyroscopy and Navigation, and the founding chair of the IEEE Inertial Sensors. He was awarded the Office of the Secretary of Defense Medal for Exceptional Public Service in 2013, and the 2009 IEEE Sensors Council Technical Achievement Award. He is the President of the IEEE Sensors Council and the IEEE Fellow. Author Biographies xi List of Figures xiii List of Tables xix 1 Introduction 1 1.1 Navigation 1 1.2 Inertial Navigation 2 1.3 Pedestrian Inertial Navigation 5 1.3.1 Approaches 6 1.3.2 IMU Mounting Positions 7 1.3.3 Summary 8 1.4 Aiding Techniques for Inertial Navigation 9 1.4.1 Non-self-contained Aiding Techniques 9 1.4.1.1 Aiding Techniques Based on Natural Signals 9 1.4.1.2 Aiding Techniques Based on Artificial Signals 10 1.4.2 Self-contained Aiding Techniques 11 1.5 Outline of the Book 13 References 13 2 Inertial Sensors and Inertial Measurement Units 17 2.1 Accelerometers 17 2.1.1 Static Accelerometers 17 2.1.2 Resonant Accelerometers 19 2.2 Gyroscopes 21 2.2.1 Mechanical Gyroscopes 21 2.2.2 Optical Gyroscopes 22 2.2.2.1 Ring Laser Gyroscopes 22 2.2.2.2 Fiber Optic Gyroscopes 23 2.2.3 Nuclear Magnetic Resonance Gyroscopes 24 2.2.4 MEMS Vibratory Gyroscopes 24 2.2.4.1 Principle of Operation 25 2.2.4.2 Mode of Operation 25 2.2.4.3 Error Analysis 27 2.3 Inertial Measurement Units 28 2.3.1 Multi-sensor Assembly Approach 28 2.3.2 Single-Chip Approach 29 2.3.3 Device Folding Approach 30 2.3.4 Chip-Stacking Approach 31 2.4 Conclusions 32 References 32 3 Strapdown Inertial Navigation Mechanism 37 3.1 Reference Frame 37 3.2 Navigation Mechanism in the Inertial Frame 38 3.3 Navigation Mechanism in the Navigation Frame 40 3.4 Initialization 41 3.4.1 Tilt Sensing 42 3.4.2 Gyrocompassing 43 3.4.3 Magnetic Heading Estimation 44 3.5 Conclusions 45 References 45 4 Navigation Error Analysis in Strapdown Inertial Navigation 47 4.1 Error Source Analysis 47 4.1.1 Inertial Sensor Errors 48 4.1.2 Assembly Errors 51 4.1.3 Definition of IMU Grades 53 4.1.3.1 Consumer Grade 54 4.1.3.2 Industrial Grade 54 4.1.3.3 Tactical Grade 55 4.1.3.4 Navigation Grade 55 4.2 IMU Error Reduction 55 4.2.1 Six-Position Calibration 55 4.2.2 Multi-position Calibration 57 4.3 Error Accumulation Analysis 57 4.3.1 Error Propagation in Two-Dimensional Navigation 58 4.3.2 Error Propagation in Navigation Frame 61 4.4 Conclusions 62 References 63 5 Zero-Velocity Update Aided Pedestrian Inertial Navigation 65 5.1 Zero-Velocity Update Overview 65 5.2 Zero-Velocity Update Algorithm 68 5.2.1 Extended Kalman Filter 68 5.2.2 EKF in Pedestrian Inertial Navigation 70 5.2.3 Zero-Velocity Update Implementation 70 5.3 Parameter Selection 73 5.4 Conclusions 76 References 76 6 Navigation Error Analysis in the ZUPT-Aided Pedestrian Inertial Navigation 79 6.1 Human Gait Biomechanical Model 79 6.1.1 Foot Motion in Torso Frame 80 6.1.2 Foot Motion in Navigation Frame 80 6.1.3 Parameterization of Trajectory 81 6.2 Navigation Error Analysis 83 6.2.1 Starting Point 83 6.2.2 Covariance Increase During Swing Phase 84 6.2.3 Covariance Decrease During the Stance Phase 87 6.2.4 Covariance Level Estimation 88 6.2.5 Observations 92 6.3 Verification of Analysis 93 6.3.1 Numerical Verification 93 6.3.1.1 Effect of ARW 93 6.3.1.2 Effect of VRW 95 6.3.1.3 Effect of RRW 95 6.3.2 Experimental Verification 96 6.4 Limitations of the ZUPT Aiding Technique 99 6.5 Conclusions 100 References 101 7 Navigation Error Reduction in the ZUPT-Aided Pedestrian Inertial Navigation 103 7.1 IMU-Mounting Position Selection 104 7.1.1 Data Collection 105 7.1.2 Data Averaging 105 7.1.3 Data Processing Summary 107 7.1.4 Experimental Verification 109 7.2 Residual Velocity Calibration 110 7.3 Gyroscope G-Sensitivity Calibration 115 7.4 Navigation Error Compensation Results 117 7.5 Conclusions 119 References 119 8 Adaptive ZUPT-Aided Pedestrian Inertial Navigation 121 8.1 Floor Type Detection 121 8.1.1 Algorithm Overview 122 8.1.2 Algorithm Implementation 123 8.1.2.1 Data Partition 123 8.1.2.2 Principal Component Analysis 124 8.1.2.3 Artificial Neural Network 125 8.1.2.4 Multiple Model EKF 127 8.1.3 Navigation Result 129 8.1.4 Summary 130 8.2 Adaptive Stance Phase Detection 130 8.2.1 Zero-Velocity Detector 131 8.2.2 Adaptive Threshold Determination 131 8.2.3 Experimental Verification 135 8.2.4 Summary 136 8.3 Conclusions 138 References 139 9 Sensor Fusion Approaches 141 9.1 Magnetometry 141 9.2 Altimetry 142 9.3 Computer Vision 143 9.4 Multiple-IMU Approach 145 9.5 Ranging Techniques 146 9.5.1 Introduction to Ranging Techniques 147 9.5.1.1 Time of Arrival 147 9.5.1.2 Received Signal Strength 147 9.5.1.3 Angle of Arrival 148 9.5.2 Ultrasonic Ranging 149 9.5.2.1 Foot-to-Foot Ranging 150 9.5.2.2 Directional Ranging 150 9.5.3 Ultrawide Band Ranging 153 9.6 Conclusions 154 References 154 10 Perspective on Pedestrian Inertial Navigation Systems 159 10.1 Hardware Development 159 10.2 Software Development 161 10.3 Conclusions 161 References 162 Index 163