The Present and Future of Indoor Navigation

"The Present and Future of Indoor Navigation" provides a complete overview of the latest indoor navigation technologies, algorithms, and systems. It begins by discussing various types of sensors that can be used for indoor navigation, such as accelerometers, gyroscopes, barometers, magnetometers, and cameras. It covers the various algorithms that can be used to compute the navigation solution, including Kalman filtering, particle filtering, and machine learning. Also discusses the system implementation considerations for indoor navigation, such as infrastructure, data fusion, and security. The book's focus is on present technologies and algorithms, but it also provides a look into the future possibilities for indoor navigation, making it a great resource for a wide audience. This includes researchers, engineers, and students who are interested in indoor navigation. It is also a valuable resource for anyone who wants to learn more about the latest technologies and algorithms for indoor navigation. The Present and Future of Indoor Navigation Contents 1 Introduction 1.1 Overview 1.2 Preliminaries 1.2.1 Fundamental Means of Indoor Positioning: Measurements, Data, and Tools 1.2.2 Navigation Performance Metrics 1.2.3 Absolute and Relative Positioning 1.2.4 Coordinate Frames 1.2.5 Basic Statistics 1.2.6 Contents of the Book References 2 Positioning Measurements, Sensors, and Their Errors 2.1 Radio Signals 2.1.1 GSM 2.1.2 UMTS 2.1.3 LTE 2.1.4 5G NR 2.1.5 Wi-Fi 2.1.6 Bluetooth 2.1.7 Ultrawideband 2.1.8 High-Sensitivity GNSS 2.2 Sensors 2.2.1 Inertial Sensors 2.2.2 Magnetometers 2.2.3 Barometers 2.2.4 Optical Sensors and Systems 2.2.5 Future Trends 2.3 Computer Vision 2.3.1 Feature Detection and Matching 2.3.2 Optical Flow 2.3.3 Perspective Projection and Epipolar Geometry 2.3.4 Error Sources in Computer Vision 2.3.5 Visual Odometry 2.3.6 Indoor Navigation-Specific Features 2.3.7 Future Trends 2.4 Summary References 3 Positioning and Navigation Algorithms 3.1 From Measurements to Position: Static Positioning 3.1.1 Ranging 3.1.2 Angle of Arrival 3.1.3 Strapdown Inertial Navigation 3.2 Theoretical Error Analysis 3.2.1 Fisher Information and Estimation Error Bounds 3.2.2 Error Bound for Propagation Time Estimation 3.2.3 Error Bound for Angle Estimation 3.2.4 Position Error Bound 3.3 Least-Squares Estimation 3.3.1 Gauss–Newton Method for Nonlinear Least Squares 3.3.2 Trilaterion Using Least-Squares Estimation 3.4 Fingerprinting 3.4.1 Creating the Database 3.4.2 RSSI-Based Positioning 3.5 Dead Reckoning 3.5.1 Pedestrian Dead Reckoning 3.6 Time Series Estimation 3.6.1 Bayesian Filtering 3.6.2 Kalman Filtering 3.6.3 Particle Filtering 3.6.4 Factor Graph Optimization 3.7 The Future of Navigation Algorithms: Machine Learning 3.7.1 Unsupervised, Supervised, and Reinforcement Learning 3.7.2 Machine Learning for Indoor Navigation 3.8 Summary References 4 Navigation System Setup 4.1 Maps 4.1.1 Map Matching with Particle Filter 4.1.2 Graph-Based Map Constraints 4.2 Simultaneous Localization and Mapping 4.2.1 Probabilistic SLAM 4.2.2 Visual SLAM 4.2.3 SLAM with Nonvisual Positioning Data 4.3 Cooperative Navigation 4.3.1 Centralized and Noncentralized Calculation 4.3.2 Measuring the Range Between Users 4.3.3 Computing the Cooperative Navigation Solution 4.4 Computer Vision-Based Tracking 4.4.1 Tracking Pipeline 4.4.2 The Future of Tracking 4.5 Radio-Based Indoor Positioning 4.5.1 Channel Modeli 4.5.2 Description of the Simulated Positioning System 4.5.3 Brief Description of the Measurements and the Utilized EKF 4.5.4 Positioning with CRB-Based Measurements 4.5.5 Positioning with Practical Channel Estimators 4.6 Summary References List of Abbreviations List of Symbols About the Authors Index