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The 1st International Workshop on the Quality of Geodetic Observation and Monitoring Systems (QuGOMS'11)

E-BookPDF1 - PDF WatermarkE-Book
183 Seiten
Englisch
Springer International Publishingerschienen am06.12.20142015
These proceedings contain 25 papers, which are the peer-reviewed versions of presentations made at the 1st International Workshop on the Quality of Geodetic Observation and Monitoring (QuGOMS'11), held 13 April to 15 April 2011 in Garching, Germany. The papers were drawn from five sessions which reflected the following topic areas: (1) Uncertainty Modeling of Geodetic Data, (2) Theoretical Studies on Combination Strategies and Parameter Estimation, (3) Recursive State-Space Filtering, (4) Sensor Networks and Multi Sensor Systems in Engineering Geodesy, (5) Multi-Mission Approaches With View to Physical Processes in the Earth System.mehr
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Produkt

KlappentextThese proceedings contain 25 papers, which are the peer-reviewed versions of presentations made at the 1st International Workshop on the Quality of Geodetic Observation and Monitoring (QuGOMS'11), held 13 April to 15 April 2011 in Garching, Germany. The papers were drawn from five sessions which reflected the following topic areas: (1) Uncertainty Modeling of Geodetic Data, (2) Theoretical Studies on Combination Strategies and Parameter Estimation, (3) Recursive State-Space Filtering, (4) Sensor Networks and Multi Sensor Systems in Engineering Geodesy, (5) Multi-Mission Approaches With View to Physical Processes in the Earth System.
Details
Weitere ISBN/GTIN9783319108285
ProduktartE-Book
EinbandartE-Book
FormatPDF
Format Hinweis1 - PDF Watermark
FormatE107
Erscheinungsjahr2014
Erscheinungsdatum06.12.2014
Auflage2015
ReiheInternational Association of Geodesy Symposia
Reihen-Nr.140
Seiten183 Seiten
SpracheEnglisch
IllustrationenVIII, 183 p. 109 illus.
Artikel-Nr.1719627
Rubriken
Genre9200

Inhalt/Kritik

Inhaltsverzeichnis
1;Preface;6
2;Contents;8
3;Part I Uncertainty Modeling of Geodetic Data;10
3.1;Modeling Data Quality Using Artificial Neural Networks;11
3.1.1;1 Introduction;12
3.1.2;2 Data Quality Propagation with Artificial Neural Networks;12
3.1.3;3 Propagation of Data Quality for Polar Point Determination;12
3.1.4;4 Propagation of Data Quality for Mobile Phone Positioning;14
3.1.5;Conclusion;16
3.1.6;References;16
3.2;Magic Square of Real Spectral and Time Series Analysis with an Application to Moving Average Processes;17
3.2.1;1 Introduction;17
3.2.2;2 Basic Elements of Stochastic Processes;18
3.2.3;3 Magic Square for Covariance-Stationary, Discrete-Time Processes;19
3.2.3.1;3.1 Time Domain (Left-Hand Side);19
3.2.3.1.1;Example: MA(q) Process;20
3.2.3.2;3.2 Frequency Domain (Right-Hand Side);20
3.2.3.2.1;Example: MA(q) Process;21
3.2.3.3;3.3 Transitions Between the Time and Frequency Domain;21
3.2.3.3.1;Example: MA(q) Process;22
3.2.4;Conclusion and Outlook;22
3.2.5;References;22
3.3;Describing the Quality of Inequality Constrained Estimates;23
3.3.1;1 Introduction and Motivation;23
3.3.2;2 Background;24
3.3.3;3 MC-QP Method;25
3.3.3.1;3.1 Propagation of the Probability Density;25
3.3.3.2;3.2 Confidence Regions (HPD Regions);25
3.3.3.3;3.3 Influence of the Constraints;26
3.3.4;4 Case Study;26
3.3.5;5 Summary and Outlook;28
3.3.6;References;28
3.4;GNSS Integer Ambiguity Validation Procedures: Sensitivity Analysis;29
3.4.1;1 Introduction;29
3.4.2;2 Ambiguity Estimation and Validation Procedure;30
3.4.2.1;2.1 Ambiguity Estimation;30
3.4.2.2;2.2 Ambiguity Validation with W-Ratio Test;31
3.4.3;3 Sensitivity Analysis for the Validation Tests;31
3.4.3.1;3.1 Description of Test Data Sets and Data Processing;31
3.4.3.2;3.2 Impact of Undetected Outliers;31
3.4.3.3;3.3 Impact of Stochastic Modeling;32
3.4.3.4;3.4 Satellite Configurations;33
3.4.4;Concluding Remarks;34
3.4.5;References;34
3.5;Optimal Design of Deformation Monitoring Networks Using the Global Optimization Methods;35
3.5.1;1 Introduction;35
3.5.2;2 The Optimal Design Problem;36
3.5.3;3 The Shuffled Frog Leaping Algorithm;36
3.5.4;4 An Example;37
3.5.5;Conclusions;38
3.5.6;References;38
4;Part II Theoretical Studies on Combination Strategies and Parameter Estimation;40
4.1;Towards the Combination of Data Sets from Various Observation Techniques;41
4.1.1;1 Introduction;41
4.1.2;2 Gauss-Markov Model;42
4.1.3;3 Combination Models;42
4.1.4;4 Numerical Example;44
4.1.5;5 Multi-scale Combination;46
4.1.6;Conclusion;48
4.1.7;References;48
4.2;On the Weighted Total Least Squares Solutions;50
4.2.1;1 Introduction;50
4.2.2;2 Mathematical Models of the TLS Problem;51
4.2.3;3 The Weighted TLS Solutions;51
4.2.3.1;3.1 Solutions Using Lagrange Multipliers;51
4.2.3.2;3.2 Solutions Using the GHM Method;52
4.2.3.3;3.3 Solutions Using the Gradient;53
4.2.4;4 Test Results;54
4.2.5;Conclusions and Further Work;54
4.2.6;Appendix;54
4.2.7;References;55
4.3;Integration of Observations and Models in a Consistent Least Squares Adjustment Model;56
4.3.1;1 Introduction;56
4.3.2;2 Integration in a Least Squares Adjustment Model;57
4.3.2.1;2.1 Functional Model;57
4.3.2.2;2.2 Stochastic Model;57
4.3.2.3;2.3 Linear Least Squares Adjustment;58
4.3.3;3 Example;58
4.3.4;Conclusions and Outlook;60
4.3.5;References;61
4.4;Comparison of Different Combination Strategies Applied for the Computation of Terrestrial Reference Frames and Geodetic Parameter Series;62
4.4.1;1 Introduction;62
4.4.2;2 Least Squares Adjustment by Gauß-Markov Model;63
4.4.3;3 Combination Strategies;64
4.4.3.1;3.1 Combination on Observation Level;65
4.4.3.2;3.2 Combination on Normal Equation Level;65
4.4.3.3;3.3 Combination on Parameter Level;66
4.4.4;4 Comparison of the Different Combination Strategies;66
4.4.5;5 Combination in the Geodetic Practice: The Realization of ITRS;67
4.4.6;Summary and Conclusions;68
4.4.7;References;69
4.5;W-Ratio Test as an Integer Aperture Estimator: Pull-in Regions and Ambiguity Validation Performance;70
4.5.1;1 Introduction;70
4.5.2;2 Parameter Estimation;71
4.5.3;3 W-Ratio Statistical Test;71
4.5.4;4 W-Ratio as an Integer Aperture Estimator;72
4.5.5;5 Numerical Analysis;73
4.5.6;Concluding Remarks;75
4.5.7;References;75
4.6;Performing 3D Similarity Transformation Using the Weighted Total Least-Squares Method;76
4.6.1;1 Introduction;76
4.6.2;2 Nonlinear 3D Similarity Transformation;77
4.6.3;3 The Nonlinear 3D Similarity Transformation Solved by the Solution Within the Nonlinear GH Model;78
4.6.4;4 Case Study;80
4.6.5;Concluding Remarks;81
4.6.6;References;82
4.7;Comparison of SpatialAnalyzer and Different Adjustment Programs;83
4.7.1;1 Introduction;83
4.7.2;2 Net Adjustment;84
4.7.2.1;2.1 SpatialAnalyzer;84
4.7.2.2;2.2 Network;84
4.7.2.3;2.3 Results;85
4.7.3;3 Form Fitting;85
4.7.4;Conclusion;87
4.7.5;References;88
5;Part III Recursive State-Space Filtering;89
5.1;State-Space Filtering with Respect to Data Imprecision and Fuzziness;90
5.1.1;1 Introduction;90
5.1.2;2 Modeling of Imprecision and Fuzziness;91
5.1.2.1;2.1 Sources of Uncertainty;91
5.1.2.2;2.2 Modeling of Uncertainty;92
5.1.3;3 Introduction to State-Space Filtering;93
5.1.3.1;3.1 System and Measurement Equation;93
5.1.3.2;3.2 Extended Kalman Filter;93
5.1.4;4 Imprecise Filter Extension;94
5.1.4.1;4.1 General Case;94
5.1.4.2;4.2 KF with Imprecise and Fuzzy Data;94
5.1.5;5 Example for Imprecise Kalman Filtering;95
5.1.6;Conclusions;97
5.1.7;References;97
5.2;Unscented Kalman Filter Algorithm with Colored Noise and Its Application in Spacecraft Attitude Estimation;98
5.2.1;1 Introduction;98
5.2.2;2 Mathematical Model of Satellite Attitude;99
5.2.2.1;2.1 Quaternion in Satellite Attitude;99
5.2.2.2;2.2 Nonlinear Model of the Satellite Attitude;99
5.2.3;3 UKF Algorithm;100
5.2.4;4 Series Representation of Colored Noise and Its Variance Calculation;101
5.2.5;5 Test Computation and Analysis;101
5.2.6;Concluding Remarks;102
5.2.7;References;103
5.3;Principles and Comparisons of Various Adaptively Robust Filters with Applications in Geodetic Positioning;104
5.3.1;1 Introduction;104
5.3.2;2 Principle of Adaptive Filtering;105
5.3.2.1;2.1 Principle of Fading Kalman Filter;105
5.3.2.2;2.2 Principle of Sage Windowing Filter;105
5.3.2.3;2.3 Principle of Robust Filter;105
5.3.2.4;2.4 Adaptively Robust Filter;106
5.3.3;3 Design of the Adaptive Factors;106
5.3.3.1;3.1 Fading Factors;106
5.3.3.2;3.2 Robust Weight Element;106
5.3.3.3;3.3 Adaptive Factors;106
5.3.4;4 Computations and Comparisons;106
5.3.5;Concluding Remarks;108
5.3.6;References;108
5.4;Alternative Nonlinear Filtering Techniques in Geodesy for Dual State and Adaptive Parameter Estimation;109
5.4.1;1 Introduction;109
5.4.2;2 Nonlinear State Estimation;110
5.4.2.1;2.1 The Probabilistic Inference;110
5.4.2.2;2.2 The Bayes Filter;111
5.4.2.3;2.3 The Extended Kalman Particle Filter;111
5.4.2.4;2.4 Combined Parameter and State Estimation in EKPF Algorithm;112
5.4.3;3 Numerical Applications;112
5.4.3.1;3.1 Tracking of a Nonlinear Trajectory;112
5.4.3.2;3.2 Tracking of a Nonlinear Trajectory;113
5.4.4;Conclusion;115
5.4.5;References;115
6;Part IV Sensor Networks and Multi Sensor Systems in Engineering Geodesy;116
6.1;Parametric Modeling of Static and Dynamic Processes in Engineering Geodesy;117
6.1.1;1 Introduction;117
6.1.2;2 Parametric Modeling of Deformation Processes;118
6.1.3;3 Case Study: The Mass Movement `Steinlehnen';119
6.1.3.1;3.1 Monitoring System;119
6.1.3.2;3.2 Parametric Slope Model;119
6.1.4;4 Parametric Identification of a Simplified Scarp;121
6.1.4.1;4.1 Static Example;121
6.1.4.2;4.2 Dynamic Example;122
6.1.5;Conclusions;124
6.1.6;References;124
6.2;Land Subsidence in Mahyar Plain, Central Iran, Investigated Using EnvisatSAR Data;126
6.2.1;1 Introduction;126
6.2.2;2 Study Area;127
6.2.3;3 Methodology;127
6.2.4;4 Experimental Results and Analysis;127
6.2.4.1;4.1 InSAR Result;127
6.2.4.2;4.2 Earth Fissures;128
6.2.4.3;4.3 Piezometric Records;128
6.2.5;Conclusions;128
6.2.6;References;129
6.3;Recent Impacts of Sensor Network Technology on Engineering Geodesy;130
6.3.1;1 Introduction;130
6.3.2;2 Geo Sensor Network Technology;131
6.3.2.1;2.1 General Statements;131
6.3.2.2;2.2 Sensing;132
6.3.2.3;2.3 Communication;132
6.3.2.4;2.4 Network Operating;133
6.3.3;3 Selected Aspects;134
6.3.3.1;3.1 Calibration of Motes: An Example;134
6.3.3.2;3.2 Carrier Phase Based Positioning Using Simple Navigation Receivers;134
6.3.4;Conclusion and Outlook;136
6.3.5;References;136
6.4;Design of Artificial Neural Networks for Change-Point Detection;138
6.4.1;1 Introduction;138
6.4.2;2 Artificial Neural Networks;139
6.4.3;3 Change-Point Detection by ANN;140
6.4.4;4 Results and Practical Considerations;141
6.4.5;5 Summary and Outlook;142
6.4.6;References;143
6.5;Spatial and Temporal Kinematics of the Inylchek Glacier in Kyrgyzstan Derived from Landsat and ASTER Imagery;144
6.5.1;1 Introduction;144
6.5.2;2 Data and Methodology;145
6.5.2.1;2.1 ASTER Imagery;145
6.5.2.2;2.2 Landsat Imagery;146
6.5.3;3 Results;146
6.5.4;Conclusion;147
6.5.5;References;147
6.6;Response Automation in Geodetic Sensor Networks by Means of BayesianNetworks;149
6.6.1;1 Introduction;149
6.6.2;2 State Description;150
6.6.3;3 Bayesian Networks for the Assessment of Events;150
6.6.4;4 Soft Evidence by Kalman Filter;151
6.6.5;5 Simulation Results;152
6.6.6;Conclusion;153
6.6.7;References;154
6.7;Efficiency Optimization of Surveying Processes;155
6.7.1;1 Introduction;155
6.7.2;2 Process Modeling and Simulation;156
6.7.2.1;2.1 Elements of a Petri Net;156
6.7.2.2;2.2 Timed Petri Nets;156
6.7.2.3;2.3 Run of a Petri Net;156
6.7.3;3 Optimization;157
6.7.4;4 Time Model;157
6.7.5;5 Numerical Study and Results;158
6.7.6;6 Summary and Outlook;160
6.7.7;References;160
6.8;Modeling and Propagation of Quality Parameters in Engineering Geodesy Processes in Civil Engineering;161
6.8.1;1 Introduction;161
6.8.2;2 Quality Model;162
6.8.2.1;2.1 Fundamentals;162
6.8.2.2;2.2 Exemplary Parameters;162
6.8.3;3 Quality Propagation Methods;163
6.8.3.1;3.1 General;163
6.8.3.2;3.2 Standard Deviation;163
6.8.3.3;3.3 Tolerance Correctness;164
6.8.4;4 Simulation Results;164
6.8.4.1;4.1 Example: Basement Walls;164
6.8.4.2;4.2 Different Simulation Scenarios;164
6.8.5;Conclusions;166
6.8.6;References;166
7;Part V Multi-Mission Approaches with View to Physical Processes in the Earth System;167
7.1;Completion of Band-Limited Data Sets on the Sphere;168
7.1.1;1 Introduction;168
7.1.2;2 Stochastic Processes on the Sphere;169
7.1.3;3 Model Building and Separability;171
7.1.4;4 Application and Simulations;171
7.1.5;Summary and Conclusions;173
7.1.6;Appendix;173
7.1.6.1;Expectation and Variance of a Stochastic Process in Amplitude/Phase Notation on the Sphere;173
7.1.7;References;175
8;List of Reviewers;176
9;Author Index;177mehr