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室内定位与导航(英文版)(INDOOR POSITIONING AND NAVIGATION) 科学出版社书籍详细信息
- ISBN：9787030596260
- 作者：暂无作者
- 出版社：暂无出版社
- 出版时间：2019-03
- 页数：240
- 价格：66.70
- 纸张：胶版纸
- 装帧：平装-胶订
- 开本：16开
- 语言：未知
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内容简介：
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.
书籍目录：
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 Construction Algorithm
4.6.2 Clustering Based on LVQ
4.6.3 Dimension Reduction based on MDS
4.6.4 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 Equations
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
6.4 Location Error Analysis
6.4.1 Offset from TOA Estimation Technique
6.4.2 Measurement Error
6.4.3 NLOS Propagation
6.4.4 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 Compositions
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 Relationship Model for Indoor Spaces
11.2.1 Indoor Spatial Area Types
11.2.2 Indoor Topological Elements
11.2.3 Presentation of Topological Relationship
11.3 Constructing Indoor Network Based on Spatial Topological Relation
11.3.1 Further Subdivision of BA Unit
11.3.2 Indoor Spatial Topographical Relationships
11.3.3 Procedure for Constructing Indoor Spatial Network
11.3.4 Experiments
11.4 Organization and Scheduling of Indoor 3D Model Based on Topological Relations
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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