室内定位与导航（英文版）（Indoor Positioning and Navigation）
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- 作者：Kegen Yu[编]
- 出版时间：2019/3/1
- ISBN：9787030596260
- 出 版 社：科学出版社
- 中图法分类：TN
- 页码：240
- 纸张：
- 版次：01
- 开本：16
Contents
Chapter 1 Introduction 1
1.1 Application Scenarios of Positioning and Navigation 1
1.2 Brief History of Indoor Positioning and Navigation 3
1.3 Overview of the Book 7
References 8
Chapter 2 Major Signal Parameters 10
2.1 Introduction 10
2.2 Received Signal Strength 11
2.3 Time of Arrival 12
2.3.1 Effect of Bandlimiting 13
2.3.2 Multipath Effect 13
2.3.3 Special Acoustic Signal 15
2.4 Angle of Arrival 16
2.4.1 Signal Processing for AOA Estimation 16
2.4.2 Beamforming for Signal Processing 16
2.4.3 TDOA for AOA Estimation 18
2.5 Range 18
2.5.1 Round-Trip Time-Based Ranging 18
2.5.2 TDOA-Based Ranging 20
2.5.3 RSS-Based Ranging 20
2.5.4 Pseudorange 21
2.6 INS Parameters 22
2.6.1 Acceleration 22
2.6.2 Turning Rate 23
2.7 Carrier Phase 24
2.8 Frequency Offset 24
2.9 Internal Radio Delay 25
2.10 Signal-to-Noise Ratio 26
References 27
Chapter 3 MEMS Sensor and Pedestrian Dead Reckoning 29
3.1 MEMS Technology 29
3.1.1 Introduction to MEMS 29
3.1.2 History of MEMS Technology 29
3.1.3 Application of MEMS Technology 31
3.2 MEMS Accelerometer and Gyroscope 37
3.2.1 MEMS Micro Accelerometer 37
3.2.2 MEMS Gyroscope 41
3.3 Pedestrian Dead Reckoning 44
3.3.1 Basic Principles 45
3.3.2 Example 49
References 51
Chapter 4 RFID Indoor Localization Techniques 53
4.1 Introduction 53
4.2 Localization Based on Improved Ranging Method 54
4.2.1 Ranging Algorithm Based on Similarity Analysis 54
4.2.2 Experimental Results 56
4.3 Localization based on Residual Weighted Multi-Dimensional Scaling 57
4.3.1 Weighted Multi-Dimensional Scaling Algorithm 58
4.3.2 Simulation and Discussion 60
4.4 Localization based on Convex Optimization 60
4.5 Localization based on Improved Fingerprinting 62
4.5.1 Basic Principle and Structure 63
4.5.2 Localization Scene 63
4.5.3 Dimensionality Reduction based on PCA 64
4.5.4 Clustering Based on K-Means 65
4.5.5 Simulation Result and Discussion 66
4.6 Localization based on Crowdsourcing 69
4.6.1 Fingerprint Database Construction Algorithm 70
4.6.2 Clustering Based on LVQ 70
4.6.3 Dimension Reduction based on MDS 71
4.6.4 Simulation Results 72
References 74
Chapter 5 Precise Positioning Using Terrestrial Ranging Technology 77
5.1 Introduction 77
5.1.1 Overview of the Terrestrial Ranging Technology 77
5.1.2 Measurements and Measurement Equations 79
5.2 Terrestrial-Based On-The-Fly Positioning Method 81
5.2.1 Dynamic Model 81
5.2.2 Measurement Model 82
5.2.3 Calculation of Approximate Initial State 83
5.2.4 Experiment and Result Analysis 83
5.3 Indoor Positioning and Attitude Determination using
New Terrestrial Ranging Signals 88
5.3.1 Multipath Mitigation Technology 88
5.3.2 Locata Position and Attitude Computation Model 90
5.3.3 Locata PAMS Mechanization 91
5.3.4 Experiment and Analyses 93
5.4 Terrestrial Augmented GNSS Precise Point Positioning Method for
Kinematic Application 98
5.4.1 Single-Differenced GNSS Precise Point Positioning 98
5.4.2 Terrestrial Augmented PPP-GNSS System 101
5.4.3 Experiment and Result Analysis 103
References 109
Chapter 6 Ultra-Wideband-Based Indoor Localization 112
6.1 Introduction 112
6.2 Ultra-Wideband Signal 113
6.2.1 Definition of Ultra-Wideband 113
6.2.2 Advantages of Ultra-Wideband-Based Indoor Localization 114
6.3 Ultra-wideband Location Estimation 115
6.3.1 Overview 115
6.3.2 Basic Theory of Location Estimation 116
6.3.3 Non-Cooperative and Cooperative Localization Network 118
6.4 Location Error Analysis 118
6.4.1 Offset from TOA Estimation Technique 119
6.4.2 Measurement Error 120
6.4.3 NLOS Propagation 121
6.4.4 Offset from Non-Linear Least Squares Algorithm 123
6.5 Integrated with Inertial Navigation System 124
6.5.1 UWB and INS Integration Schemes 125
6.5.2 Three Issues in UWB/INS Integration 127
6.6 Case Studies 130
6.6.1 UWB Indoor Localization 130
6.6.2 UWB/INS Tightly-Coupled Integration for Localization 132
References 135
Chapter 7 Indoor Positioning Technology Based on LED Visible Lights 137
7.1 Introduction 137
7.2 Principle and Composition 138
7.2.1 Basic Principle 138
7.2.2 Composition of System 139
7.3 Encoding and Identification of Information 141
7.3.1 Encoding of information 141
7.3.2 Identification of Information 142
7.4 Positioning Methods 143
7.4.1 The Nearest Neighbor Method 143
7.4.2 Geometric Analytic Method 144
7.4.3 Scenario Analysis 148
7.4.4 Camera-Based Positioning Method 149
7.5 Experiments and Results 150
7.5.1 Experimental System and Environment 151
7.5.2 Experimental Procedures 153
7.5.3 Experimental Results 153
References 156
Chapter 8 Positioning Based on Geomagnetic Field 158
8.1 Properties of Geomagnetic Field 158
8.1.1 Basic Compositions 158
8.1.2 Basic Elements of Geomagnetic Field 158
8.1.3 Geomagnetic Field Model and Geomagnetic Map 159
8.1.4 Geomagnetic Anomaly 160
8.1.5 Characteristics of Geomagnetic Field in Indoor Environment 161
8.2 Establishment of Indoor Magnetic Fingerprint Database 162
8.2.1 Calibration of Magnetometer 162
8.2.2 Collection of Magnetometer Readings 164
8.2.3 Post-processing of Raw Measurements 164
8.2.4 Establishment of Geomagnetic Fingerprint Database 165
8.3 Geomagnetic Matching Model 167
8.3.1 Minimum Distance Method 168
8.3.2 Correlation Measurement Method 168
8.3.3 Hausdorff Distance Method 169
8.3.4 Dynamic Time Warping Method 169
8.3.5 Geomagnetic Matching Algorithm Based on Particle Filter 170
References 172
Chapter 9 LIDAR- and Vision-Based Positioning 174
9.1 Introduction 174
9.2 Fundamentals of Mobile-Robot Motion-Sensing System 175
9.3 Principle of Vision-Based Positioning 179
9.3.1 Epipolar Geometry 179
9.3.2 Basic Matrix 181
9.3.3 Essential Matrix 182
9.3.4 Intersection Camera 183
9.4 Localization Algorithm 184
9.4.1 Kalman filter 184
9.4.2 Particle Filter 186
9.5 SLAM and LiDAR SLAM 187
9.5.1 SLAM 188
9.5.2 LiDAR SLAM 189
Reference 192
Chapter 10 Integration Algorithms for All Source Positioning and Navigation 194
10.1 Introduction 194
10.2 System Design and Algorithms for ASPN 195
10.2.1 An overview of the ASPN program 195
10.2.2 Indoor Navigation Technologies 196
10.2.3 Integration Architectures 198
10.2.4 Global and Local Optimal Data Fusion Methods 198
10.3 State Dynamic Modeling for Kalman Filtering 202
10.3.1 Dynamic Models 202
10.3.2 Information Sharing between Local and Master Filters 203
10.4 Case Study 206
10.4.1 Simulation Tests 206
10.4.2 Field Tests 211
References 214
Chapter 11 Indoor Network Models for Indoor Navigation 216
11.1 Introduction 216
11.2 Topographical Relationship Model for Indoor Spaces 217
11.2.1 Indoor Spatial Area Types 217
11.2.2 Indoor Topological Elements 218
11.2.3 Presentation of Topological Relationship 219
11.3 Constructing Indoor Network Based on Spatial Topological Relation 222
11.3.1 Further Subdivision of BA Unit 222
11.3.2 Indoor Spatial Topographical Relationships 224
11.3.3 Procedure for Constructing Indoor Spatial Network 224
11.3.4 Experiments 226
11.4 Organization and Scheduling of Indoor 3D Model Based on
Topological Relations 229
11.4.1 Indoor Spatial Topology Relation Model 230
11.4.2 Self-Adaptive Adjustment of View Frustum Based on Porches 230
11.4.3 Experiments 232
References 234
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