The Present and Future of 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