Pedestrian Inertial Navigation with Self-Contained Aiding

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 xiList of Figures xiiiList of Tables xix1 Introduction 11.1 Navigation 11.2 Inertial Navigation 21.3 Pedestrian Inertial Navigation 51.3.1 Approaches 61.3.2 IMU Mounting Positions 71.3.3 Summary 81.4 Aiding Techniques for Inertial Navigation 91.4.1 Non-self-contained Aiding Techniques 91.4.1.1 Aiding Techniques Based on Natural Signals 91.4.1.2 Aiding Techniques Based on Artificial Signals 101.4.2 Self-contained Aiding Techniques 111.5 Outline of the Book 13References 132 Inertial Sensors and Inertial Measurement Units 172.1 Accelerometers 172.1.1 Static Accelerometers 172.1.2 Resonant Accelerometers 192.2 Gyroscopes 212.2.1 Mechanical Gyroscopes 212.2.2 Optical Gyroscopes 222.2.2.1 Ring Laser Gyroscopes 222.2.2.2 Fiber Optic Gyroscopes 232.2.3 Nuclear Magnetic Resonance Gyroscopes 242.2.4 MEMS Vibratory Gyroscopes 242.2.4.1 Principle of Operation 252.2.4.2 Mode of Operation 252.2.4.3 Error Analysis 272.3 Inertial Measurement Units 282.3.1 Multi-sensor Assembly Approach 282.3.2 Single-Chip Approach 292.3.3 Device Folding Approach 302.3.4 Chip-Stacking Approach 312.4 Conclusions 32References 323 Strapdown Inertial Navigation Mechanism 373.1 Reference Frame 373.2 Navigation Mechanism in the Inertial Frame 383.3 Navigation Mechanism in the Navigation Frame 403.4 Initialization 413.4.1 Tilt Sensing 423.4.2 Gyrocompassing 433.4.3 Magnetic Heading Estimation 443.5 Conclusions 45References 454 Navigation Error Analysis in Strapdown Inertial Navigation 474.1 Error Source Analysis 474.1.1 Inertial Sensor Errors 484.1.2 Assembly Errors 514.1.3 Definition of IMU Grades 534.1.3.1 Consumer Grade 544.1.3.2 Industrial Grade 544.1.3.3 Tactical Grade 554.1.3.4 Navigation Grade 554.2 IMU Error Reduction 554.2.1 Six-Position Calibration 554.2.2 Multi-position Calibration 574.3 Error Accumulation Analysis 574.3.1 Error Propagation in Two-Dimensional Navigation 584.3.2 Error Propagation in Navigation Frame 614.4 Conclusions 62References 635 Zero-Velocity Update Aided Pedestrian Inertial Navigation 655.1 Zero-Velocity Update Overview 655.2 Zero-Velocity Update Algorithm 685.2.1 Extended Kalman Filter 685.2.2 EKF in Pedestrian Inertial Navigation 705.2.3 Zero-Velocity Update Implementation 705.3 Parameter Selection 735.4 Conclusions 76References 766 Navigation Error Analysis in the ZUPT-Aided Pedestrian Inertial Navigation 796.1 Human Gait Biomechanical Model 796.1.1 Foot Motion in Torso Frame 806.1.2 Foot Motion in Navigation Frame 806.1.3 Parameterization of Trajectory 816.2 Navigation Error Analysis 836.2.1 Starting Point 836.2.2 Covariance Increase During Swing Phase 846.2.3 Covariance Decrease During the Stance Phase 876.2.4 Covariance Level Estimation 886.2.5 Observations 926.3 Verification of Analysis 936.3.1 Numerical Verification 936.3.1.1 Effect of ARW 936.3.1.2 Effect of VRW 956.3.1.3 Effect of RRW 956.3.2 Experimental Verification 966.4 Limitations of the ZUPT Aiding Technique 996.5 Conclusions 100References 1017 Navigation Error Reduction in the ZUPT-Aided Pedestrian Inertial Navigation 1037.1 IMU-Mounting Position Selection 1047.1.1 Data Collection 1057.1.2 Data Averaging 1057.1.3 Data Processing Summary 1077.1.4 Experimental Verification 1097.2 Residual Velocity Calibration 1107.3 Gyroscope G-Sensitivity Calibration 1157.4 Navigation Error Compensation Results 1177.5 Conclusions 119References 1198 Adaptive ZUPT-Aided Pedestrian Inertial Navigation 1218.1 Floor Type Detection 1218.1.1 Algorithm Overview 1228.1.2 Algorithm Implementation 1238.1.2.1 Data Partition 1238.1.2.2 Principal Component Analysis 1248.1.2.3 Artificial Neural Network 1258.1.2.4 Multiple Model EKF 1278.1.3 Navigation Result 1298.1.4 Summary 1308.2 Adaptive Stance Phase Detection 1308.2.1 Zero-Velocity Detector 1318.2.2 Adaptive Threshold Determination 1318.2.3 Experimental Verification 1358.2.4 Summary 1368.3 Conclusions 138References 1399 Sensor Fusion Approaches 1419.1 Magnetometry 1419.2 Altimetry 1429.3 Computer Vision 1439.4 Multiple-IMU Approach 1459.5 Ranging Techniques 1469.5.1 Introduction to Ranging Techniques 1479.5.1.1 Time of Arrival 1479.5.1.2 Received Signal Strength 1479.5.1.3 Angle of Arrival 1489.5.2 Ultrasonic Ranging 1499.5.2.1 Foot-to-Foot Ranging 1509.5.2.2 Directional Ranging 1509.5.3 Ultrawide Band Ranging 1539.6 Conclusions 154References 15410 Perspective on Pedestrian Inertial Navigation Systems 15910.1 Hardware Development 15910.2 Software Development 16110.3 Conclusions 161References 162Index 163