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室内定位与导航(英文版)

  • 定价: ¥98
  • ISBN:9787030596260
  • 开 本:16开 平装
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  • 折扣:
  • 出版社:科学
  • 页数:237页
  • 作者:编者:Kegen Yu
  • 立即节省:
  • 2018-01-01 第1版
  • 2018-01-01 第1次印刷
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导语

  

内容提要

  

    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.

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  Special 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