Intelligent Positioning: GIS–GPS Unification

GIS and GPS integration is happening in research and commercial activities worldwide, however this is the first GIS–GPS integration book to look at applications that combine GIS and GPS to provide one solution. It begins by providing readers with technical overviews of GPS and GIS and their integration, and then focuses on a selection of R&D activities in applications ranging from intelligent transport systems to real time location based tourist information systems.Provides overview chapters on GIS, GPS and GIS–GPS integration for readers who are less familiar with either systemBased on the authors’ own research and development activities in both the UK and the USIncludes case studies in each chapter to illustrate the end–product/commercial activities that research can lead to

Spis treści:
Foreword.
Preface.
Acknowledgements.
List of Abbreviations.
Introduction.
1. Do You Really Know Where You Are?
2. How Active Is Your Map?
3. Levels of GPS–GIS Integration.
4. Overview of the Book.
1 GIS: An Overview.
1. Introduction.
2. GIS.
2.1. The Basic Idea.
3. Functionality.
3.1. Input.
3.2. Storage.
3.3. Analysis.
3.4. Output.
4. Fundamental Concepts.
4.1. Features.
4.2. Spatial Elements.
4.3. Attribute Information.
5. Spatial and Geographical Data.
5.1. Spatial Referencing.
6. Spatial Data Modelling.
7. Spatial Data Visualization.
8. GIS and the Internet.
9. The Application of GIS.
9.1. Example GIS Applications.
10. Conclusion.
2 GPS: An Introduction.
1. GPS Description.
1.1. The Basic Idea.
1.2. The GPS Segments.
1.3. The GPS Signals.
2. The Pseudorange Observable.
2.1. Code Generation.
2.2. Autocorrelation Technique.
2.3. Pseudorange Observation Equations.
3. Point Positioning Using Pseudorange.
3.1. Least Squares Estimation.
3.2. Error Computation.
4. The Carrier Phase Observable.
4.1. Concepts.
4.2. Carrier Phase Observation Model.
4.3. Differencing Techniques.
5. Relative Positioning Using Carrier Phase.
5.1. Selection of Observations.
5.2. Baseline Solution Using Double Differences.
5.3. Stochastic Model.
6. Introducing High Precision GPS Geodesy.
6.1. High Precision Software.
6.2. Sources of Data and Information.
7. Conclusion.
3 Datum Transformations and Projections.
1. Integration Requirements.
2. Global Reference Systems.
2.1. WGS–84 Cartesian Coordinates.
2.2. International Terrestrial Reference System (ITRS).
2.3. WGS–84 Ellipsoidal Coordinates.
2.4. Cartesian to Ellipsoidal Transformation.
2.5. Ellipsoidal to Cartesian Transformation.
2.6. Relative Coordinates: Cartesian to Topocentric.
2.7. GPS Estimated Errors: Cartesian to Topocentric.
2.8. Dilution of Precision.
3. Regional Reference Systems.
3.1. Regional Ellipsoidal Coordinates.
3.2. Plane Coordinates.
3.3. Converting Latitude and Longitude to UTM.
3.4. Orthometric Height ‘Above Sea Level’.
4. Conclusion.
4 Commercial Applications That Integrate GIS and GPS.
1. Introduction.
2. National GIS/GPS Integration Team.
3. GIS and GPS Deformation Monitoring.
4. Location Based Services.
5. Intelligent Transport Systems.
6. Accessible Rural Public Transport (Case Study).
6.1. Overview.
6.2. Integrated Rural Transport.
6.3. Route Tracking System.
6.4. Conclusion.
7. Realtime Passenger Information and Bus Priority System.
8. Precision Farming.
9. Conclusion.
9.1. Shallow Integration.
9.2. Deep Integration.
5 GPS–GIS Map Matching: Combined Positioning Solution.
1. Introduction.
2. Map–Matching Methodologies.
3. Road Reduction Filter (RRF) Map–Matching Algorithm.
3.1. Introduction.
3.2. The Algorithm.
3.3. Determin ing the Correct Road Centre–line.
4. Testing VDGPS.
4.1. Testing Methodology.
4.2. Test Results.
5. Conclusion.
6 Intelligent Map Matching Using ‘Mapping Dilution of Precision’ (MDOP).
1. Introduction.
2. Least Squares Estimation of Position Error Vector.
3. Quantifying Road Geometry: Mapping Dilution of Precision (MDOP).
4. MDOP for Basic Road Shapes.
5. Testing MDOP.
6. RRF Map–Matching Enhancement.
7. Conclusion.
7 The Use of Digital Terrain Models to Aid GPS Vehicle Navigation.
1. Introduction.
2. Digital Terrain Models.
3. Spatial Interpolation of Elevation Data.
3.1. Patchwise Polynomial Interpolation.
3.2. Bicubic Interpolation.
3.3. Biquintic Interpolation.
4. Map Matching and the Road Reduction Filter.
4.1. Road Reduction Filter (RRF).
5. Data Collection and Processing.
5.1. Accuracy of Solution.
6. Results.
6.1. Height Errors – Test 1.
6.2. Position Errors – Test 1.
7. Results from Test 2 Data with a Subset of Satellites.
7.1. Position Error – Test 2.
8. Conclusion.
8 GPS Accuracy Estimation Using Map–Matching Techniques: Application to Vehicle Positioning and Odometer Calibration.
1. Introduction.
2. Methodology.
3. Map Matching.
4. Distance Correction Factor.
5. Estimating C.
5.1. Weighting Scheme for w<sub>i</sub>.
5.2. Implementing the Correction Factor Algorithm.
6. Calibration if GPS Data Is Recently Online.
7. Putting it all Together.
8. Alterations to the Correction Factor Algorithm.
9. Height Aiding.
10. Implementation.
11. Data Processing and Results.
12. Conclusion.
Appendix: Algorithms.
Algorithm 1 Estimate Location of Bus from Odometer Signal.
Algorithm 2 ‘Distance3d’ Function Used by Algorithm 4.
Algorithm 3 Update the Value of C<sub>t</sub>.
Algorithm 4 U pdate the Value of C.
Algorithm 5 Combine C.
Algorithm 6 Overview of Events.
Modification of Algorithm 6.
Bibliography.
Index.