Principles of Indoor Positioning and Indoor Navigation.

Format:

Book

Author/Creator:

Hsu, Li-Ta.

Language:

English

Subjects (All):

Indoor positioning systems (Wireless localization).

Multisensor data fusion.

Physical Description:

1 online resource (369 pages)

Edition:

1st ed.

Place of Publication:

Norwood : Artech House, 2025.

Summary:

This book provides a comprehensive exploration of indoor positioning and navigation systems, focusing on the principles, technologies, and methodologies used to achieve accurate indoor localization. It covers a wide range of topics, including sensing technologies, RF-based positioning, physical sensors, estimators and filters, and advanced techniques such as simultaneous localization and mapping (SLAM) and sensor fusion. The authors delve into the mathematical foundations, practical applications, and challenges of indoor navigation, offering insights into methods like Kalman filters, particle filters, and deep learning approaches. Designed for students, researchers, and engineers, the book also includes open-source resources for practical learning and development. Generated by AI.

Contents:

Principles of Indoor Positioning and Indoor Navigation

Contents

Preface

Acknowledgments

1 Introduction to Indoor Positioning and Navigation Systems

1.1 How to Select an IPIN System for an Application

1.1.1 Example 1: Proximity Marketing in a Shopping Mall

1.1.2 Example 2: Inventory Management

1.1.3 Example 3: Analytics for Foot Traffic on a City Sca

1.1.4 Example 4: Automatic Parking System for Intelligent Vehicles

1.2 Sensing Technologies Based on RF

1.2.1 Measurements

1.2.2 RF-Based Positioning Method: Geometry Model

1.2.3 Positioning Method: Feature Matching

1.3 Physical Sensors

1.3.1 Proprioceptive Sensors

1.3.2 Cameras

1.3.3 Lidar

1.3.4 Integration

1.4 Overview of This Book

1.4.1 Chapter Arrangements

1.4.2 Open-Sourced Codes and Data

References

2 Fundamentals of Indoor Positioning and Navigation Systems

2.1 Coordinate Systems and Transformations

2.1.1 Local Body Coordinate System

2.1.2 Local Map Coordinate System

2.1.3 Local Tangent Plane Coordinate System: East, North, Up and North, East, Down Coordinate System

2.1.4 Geodetic Coordinate System: Latitude, Longitude, Height Coordinate System

2.2 Attitude: Definition and Representation

2.2.1 Definition

2.2.2 Representation

2.3 Conclusions

3 Estimators and Filters for Indoor Positioning

3.1 Least Squares Estimation

3.1.1 Robust Estimators

3.2 Kalman Filters and Extensions

3.2.1 Linear Kalman Filter

3.2.2 Extended Kalman Filter

3.2.3 Iterated Extended Kalman Filter

3.2.4 Unscented Kalman Filter

3.2.5 Comparison Between the Kalman Filter and Its Variants

3.3 Particle Filters

3.4 Factor Graph Optimization

3.4.1 Numerical Optimization Methods

3.5 Comparison of Estimation Methods.

3.5.1 Qualitative Comparison of Estimation Methods for Indoor Positioning

3.5.2 Comparison of the EKF and FGO for Sensor Integr

3.6 Conclusions

4 Point Positioning by Radio Signals

4.1 Time Synchronization Methods

4.2 Direct Range-Based Indoor Positioning

4.2.1 Direct Ranging

4.2.2 Models and Estimation: Least Squares

4.3 Differential Range-Based Indoor Positioning

4.3.1 TDOA Measurement Across Beacons

4.3.2 Model and Estimation

4.3.3 Least Squares

4.3.4 Fang's Algorithms

4.3.5 Chan's Algorithms

4.4 Angle-Based Indoor Positioning

4.4.1 Model and Estimation: IWLS Estimation

4.4.2 Model and Estimation: OVE

4.4.3 Model and Estimation: 3-D PLE

4.5 Challenges for Radio Signal-Based Indoor Positioning

4.5.1 Thermal Noise on the Oscillator

4.5.2 Multipath and NLOS Effects

4.5.3 Distribution of Beacons

4.5.4 Nonlinearity Caused by an Initial Guess

5 Indoor Positioning Using Feature- Matching Methods

5.1 Fundamentals of Fingerprinting for Indoor Localization

5.1.1 Principles of Location Fingerprinting

5.1.2 Mathematical Formulation of the Fingerprint-Matching Problem

5.1.3 Deterministic Versus Probabilistic Fingerprinting

5.1.4 Advanced Enhancements in Fingerprinting

5.2 Pattern Recognition Approaches for Indoor Positioning

5.2.1 Location Estimation as a Classification Problem

5.2.2 Regression Approaches for Coordinate Estimation

5.2.3 Feature Engineering and Dimensionality Reduction

5.3 Deep Learning-Based Approaches

5.3.1 Algorithms for Localization

5.4 Key Challenges and Future Directions

5.4.1 Data Scalability and Deployment Effort

5.4.2 Context Awareness and Semantic Localization

5.4.3 Unavailability of Fingerprints: Physical Model-Based

5.5 Conclusions

References.

6 Positioning by Proprioceptive Sensors and Environmental Sensors

6.1 Inertial Navigation

6.1.1 Inertial Sensors

6.1.2 Inertial Navigation Equations

6.2 Wheel Odometry for Mobile Robots

6.2.1 Dead Reckoning Based on the Wheel Speed Sensor

6.2.2 IMU and Wheel Speed Sensor Integrated Navigation

6.2.3 Summary

6.3 Inertial Sensor-Based Pedestrian Navigation

6.3.1 Zero Velocity Updates

6.3.2 PDR

6.3.3 Summary

6.4 Environmental Sensors for Enhanced Dead Reckoning

6.4.1 Magnetometers for Heading Estimation

6.4.2 Barometer for Altitude Sensing and Integration

6.4.3 Sensor Fusion Strategies for Environmental Sensors

6.5 Error Modeling and Calibrations

6.5.1 Error Sources

6.5.2 Error Calibrations

6.6 Drift Mitigation and Corrections

6.6.1 Initialization

6.6.2 Motion Constraints for Drift Mitigation

6.6.3 External Aiding by Sensor Fusion

6.7 Conclusions

7 Indoor Simultaneous Localization and Mapping

7.1 Introduction to Simultaneous Localization and Mapping

7.1.1 The Historical Development of SLAM

7.1.2 SLAM Frameworks and Evolution

7.1.3 Comparison of Popular SLAM Implementations

7.2 General Mathematical Model of SLAM

7.3 Lidar SLAM

7.3.1 Point Cloud-Based Lidar SLAM: ICP

7.3.2 Feature-Based Lidar SLAM: NDT

7.3.3 Feature-Based Lidar SLAM: LOAM (Plane and Edge)

7.3.4 Challenges of Lidar SLAM

7.3.5 Closed-Loop Constraints

7.4 Visual SLAM

7.4.1 Monocular Camera

7.4.2 Monocular SLAM

7.4.3 RGB-D Camera

7.4.4 Challenges of a Visual SLAM

7.4.5 Integration of a Camera with Lidar SLAM

7.5 Roles of IMU in Lidar SLAMs

7.6 Conclusions

Appendix 7A: Derivation of Epipolar Constraint

8 Practical Aspects of Sensor Fusion

8.1 Loosely Coupled and Tightly Coupled

8.1.1 Loosely Coupled

8.1.2 Tightly Coupled.

8.1.3 Comparative Summary

8.2 Observability in Sensor Fusion

8.2.1 Observability

8.2.2 Unobservable States, Drift, and Solutions

8.3 Tuning of Sensor Fusion

8.3.1 Covariance Estimation Techniques

8.3.2 Thresholds

8.4 Calibration Techniques

8.4.1 Intrinsic Calibration

8.4.2 Extrinsic Calibration

8.5 Temporal Calibration

8.5.1 Synchronization

8.5.2 Measurement Timing

8.6 Efficiency

8.6.1 Real-Time Versus Offline

8.6.2 Memory Considerations

8.7 Conclusions

9 Advanced Indoor Positioning and Indoor Navigation Techniques

9.1 Crowdsourcing Mapping

9.1.1 Database: Smartphone Data, Beacon Positions, Classification, and Graph Optimization

9.1.2 Applications: Wi-Fi Maps, and Visual Positioning Systems

9.2 Collaborative Positioning

9.2.1 System Architecture: Centralized and Decentralized Collaboration

9.2.2 Technologies and Techniques Using Collaborative Positioning

9.2.3 Estimation Method for Collaborative Positioning

9.2.4 Applications

9.2.5 Limitations in Practice

9.3 Data-Driven PDR and Human Pose Estimation

9.3.1 AI for Motion Data Analysis

9.3.2 AI for PDR

9.3.3 Reconstructing Human Pose from Wearables

9.4 Radio Sensing: Communication and Sensing

9.4.1 Concept of Radio Sensing and Integrated Sensing

9.4.2 Applications of Radio Sensing

9.5 Reconfigurable Intelligent Surface

9.6 Conclusions

About the Authors

Index.

Notes:

Description based on publisher supplied metadata and other sources.

Part of the metadata in this record was created by AI, based on the text of the resource.

ISBN:

1-63081-978-6

9781630819781