室内定位与导航(英文版)(INDOOR POSITIONING AND N***IGATION) 科学出版社 mobi 下载 网盘 caj lrf pdf txt 阿里云

Kegen Yu主编的《室内定位与导航（英文版）》 examines a range of topics in indoor positioning and navigation, including ultra-wideband positioning, visible light positioning, radio-frequency identification positioning, pseudo-satellite (pseudolite) positioning,dead-reckoning positioning, indoor network modeling, geomagnetic positioning, vison-based positioning, simultaneous positioning and localization, and integrated positioning. In addition to the in-depth theoretical studies, all major chapters provide experimental results.

Chapter 1 Introduction

1.1 Application Scenarios of Positioning and Navigation

1.2 Brief History of Indoor Positioning and Navigation

1.3 Overview of the Book

References

Chapter 2 Major Signal Parameters

2.1 Introduction

2.2 Received Signal Strength

2.3 Time of Arrival

2.3.1 Effect of Bandlimiting

2.3.2 Multipath Effect

2.3.3 Spe Acoustic Signal

2.4 Angle of Arrival

2.4.1 Signal Processing for AOA Estimation

2.4.2 Beamforming for Signal Processing

2.4.3 TDOA for AOA Estimation

2.5 Range

2.5.1 Round-Trip Time-Based Ranging

2.5.2 TDOA-Based Ranging

2.5.3 RSS-Based Ranging

2.5.4 Pseudorange

2.6 INS Parameters

2.6.1 Acceleration

2.6.2 Turning Rate

2.7 Carrier Phase

2.8 Frequency Offset

2.9 Internal Radio Delay

2.10 Signal-to-Noise Ratio

References

Chapter 3 MEMS Sensor and Pedestrian Dead Reckoning

3.1 MEMS Technology

3.1.1 Introduction to MEMS

3.1.2 History of MEMS Technology

3.1.3 Application of MEMS Technology

3.2 MEMS Accelerometer and Gyroscope

3.2.1 MEMS Micro Accelerometer

3.2.2 MEMS Gyroscope

3.3 Pedestrian Dead Reckoning

3.3.1 Basic Principles

3.3.2 Example

References

Chapter 4 RFID Indoor Localization Techniques

4.1 Introduction

4.2 Localization Based on Improved Ranging Method

4.2.1 Ranging Algorithm Based on Similarity Analysis

4.2.2 Experimental Results

4.3 Localization based on Residual Weighted Multi-Dimensional Scaling

4.3.1 Weighted Multi-Dimensional Scaling Algorithm

4.3.2 Simulation and Discussion

4.4 Localization based on Convex Optimization

4.5 Localization based on Improved Fingerprinting

4.5.1 Basic Principle and Structure

4.5.2 Localization Scene

4.5.3 Dimensionality Reduction based on PCA

4.5.4 Clustering Based on K-Means

4.5.5 Simulation Result and Discussion

4.6 Localization based on Crowdsourcing

4.6.1 Fingerprint Database C***truction Algorithm

4.6.2 Clustering Based on LVQ

4.6.3 Dimension Reduction based on MDS

4.*** Simulation Results

References

Chapter 5 Precise Positioning Using Terrestrial Ranging Technology

5.1 Introduction

5.1.1 Overview of the Terrestrial Ranging Technology

5.1.2 Measurements and Measurement Equati***

5.2 Terrestrial-Based On-The-Fly Positioning Method

5.2.1 Dynamic Model

5.2.2 Measurement Model

5.2.3 Calculation of Approximate Initial State

5.2.4 Experiment and Result Analysis

5.3 Indoor Positioning and Attitude Determination using New Terrestrial Ranging Signals

5.3.1 Multipath Mitigation Technology

5.3.2 Locata Position and Attitude Computation Model

5.3.3 Locata PAMS Mechanization

5.3.4 Experiment and Analyses

5.4 Terrestrial Augmented GNSS Precise Point Positioning Method for Kinematic Application

5.4.1 Single-Differenced GNSS Precise Point Positioning

5.4.2 Terrestrial Augmented PPP-GNSS System

5.4.3 Experiment and Result Analysis

References

Chapter 6 Ultra-Wideband-Based Indoor Localization

6.1 Introduction

6.2 Ultra-Wideband Signal

6.2.1 Definition of Ultra-Wideband

6.2.2 Advantages of Ultra-Wideband-Based Indoor Localization

6.3 Ultra-wideband Location Estimation

6.3.1 Overview

6.3.2 Basic Theory of Location Estimation

6.3.3 Non-Cooperative and Cooperative Localization Network

*** Location Error Analysis

***.1 Offset from TOA Estimation Technique

***.2 Measurement Error

***.3 NLOS Propagation

***.4 Offset from Non-Linear Least Squares Algorithm

6.5 Integrated with Inertial Navigation System

6.5.1 UWB and INS Integration Schemes

6.5.2 Three Issues in UWB/INS Integration

6.6 Case Studies

6.6.1 UWB Indoor Localization

6.6.2 UWB/INS Tightly-Coupled Integration for Localization

References

Chapter 7 Indoor Positioning Technology Based on LED Visible Lights

7.1 Introduction

7.2 Principle and Composition

7.2.1 Basic Principle

7.2.2 Composition of System

7.3 Encoding and Identification of Information

7.3.1 Encoding of information

7.3.2 Identification of Information

7.4 Positioning Methods

7.4.1 The Nearest Neighbor Method

7.4.2 Geometric Analytic Method

7.4.3 Scenario Analysis

7.4.4 Camera-Based Positioning Method

7.5 Experiments and Results

7.5.1 Experimental System and Environment

7.5.2 Experimental Procedures

7.5.3 Experimental Results

References

Chapter 8 Positioning Based on Geomagnetic Field

8.1 Properties of Geomagnetic Field

8.1.1 Basic Compositi***

8.1.2 Basic Elements of Geomagnetic Field

8.1.3 Geomagnetic Field Model and Geomagnetic Map

8.1.4 Geomagnetic Anomaly

8.1.5 Characteristics of Geomagnetic Field in Indoor Environment

8.2 Establishment of Indoor Magnetic Fingerprint Database

8.2.1 Calibration of Magnetometer

8.2.2 Collection of Magnetometer Readings

8.2.3 Post-processing of Raw Measurements

8.2.4 Establishment of Geomagnetic Fingerprint Database

8.3 Geomagnetic Matching Model

8.3.1 Minimum Distance Method

8.3.2 Correlation Measurement Method

8.3.3 Hausdorff Distance Method

8.3.4 Dynamic Time Warping Method

8.3.5 Geomagnetic Matching Algorithm Based on Particle Filter

References

Chapter 9 LIDAR- and Vision-Based Positioning

9.1 Introduction

9.2 Fundamentals of Mobile-Robot Motion-Sensing System

9.3 Principle of Vision-Based Positioning

9.3.1 Epipolar Geometry

9.3.2 Basic Matrix

9.3.3 Essential Matrix

9.3.4 Intersection Camera

9.4 Localization Algorithm

9.4.1 Kalman filter

9.4.2 Particle Filter

9.5 SLAM and LiDAR SLAM

9.5.1 SLAM

9.5.2 LiDAR SLAM

Reference

Chapter 10 Integration Algorithms for AH Source Positioning and Navigation

10.1 Introduction

10.2 System Design and Algorithms for ASPN

10.2.1 An overview of the ASPN program

10.2.2 Indoor Navigation Technologies

10.2.3 Integration Architectures

10.2.4 Global and Local Optimal Data Fusion Methods

10.3 State Dynamic Modeling for Kalman Filtering

10.3.1 Dynamic Models

10.3.2 Information Sharing between Local and Master Filters

10.4 Case Study

10.4.1 Simulation Tests

10.4.2 Field Tests

References

Chapter 11 Indoor Network Models for Indoor Navigation

11.1 Introduction

11.2 Topographical Relati***hip Model for Indoor Spaces

11.2.1 Indoor Spatial Area Types

11.2.2 Indoor Topological Elements

11.2.3 Presentation of Topological Relati***hip

11.3 C***tructing Indoor Network Based on Spatial Topological Relation

11.3.1 Further Subdivision of B***nit

11.3.2 Indoor Spatial Topographical Relati***hips

11.3.3 Procedure for C***tructing Indoor Spatial Network

11.3.4 Experiments

11.4 Organization and Scheduling of Indoor 3D Model Based on Topological Relati***

11.4.1 Indoor Spatial Topology Relation Model

11.4.2 Self-Adaptive Adjustment of View Frustum Based on Porches

11.4.3 Experiments

References

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