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

Preface 6
Contents 8
Part I Uncertainty Modeling of Geodetic Data 10
Modeling Data Quality Using Artificial Neural Networks 11
1 Introduction 12
2 Data Quality Propagation with Artificial Neural Networks 12
3 Propagation of Data Quality for Polar Point Determination 12
4 Propagation of Data Quality for Mobile Phone Positioning 14
Conclusion 16
References 16
Magic Square of Real Spectral and Time Series Analysis with an Application to Moving Average Processes 17
1 Introduction 17
2 Basic Elements of Stochastic Processes 18
3 Magic Square for Covariance-Stationary, Discrete-Time Processes 19
3.1 Time Domain (Left-Hand Side) 19
Example: MA(q) Process 20
3.2 Frequency Domain (Right-Hand Side) 20
Example: MA(q) Process 21
3.3 Transitions Between the Time and Frequency Domain 21
Example: MA(q) Process 22
Conclusion and Outlook 22
References 22
Describing the Quality of Inequality Constrained Estimates 23
1 Introduction and Motivation 23
2 Background 24
3 MC-QP Method 25
3.1 Propagation of the Probability Density 25
3.2 Confidence Regions (HPD Regions) 25
3.3 Influence of the Constraints 26
4 Case Study 26
5 Summary and Outlook 28
References 28
GNSS Integer Ambiguity Validation Procedures: Sensitivity Analysis 29
1 Introduction 29
2 Ambiguity Estimation and Validation Procedure 30
2.1 Ambiguity Estimation 30
2.2 Ambiguity Validation with W-Ratio Test 31
3 Sensitivity Analysis for the Validation Tests 31
3.1 Description of Test Data Sets and Data Processing 31
3.2 Impact of Undetected Outliers 31
3.3 Impact of Stochastic Modeling 32
3.4 Satellite Configurations 33
Concluding Remarks 34
References 34
Optimal Design of Deformation Monitoring Networks Using the Global Optimization Methods 35
1 Introduction 35
2 The Optimal Design Problem 36
3 The Shuffled Frog Leaping Algorithm 36
4 An Example 37
Conclusions 38
References 38
Part II Theoretical Studies on Combination Strategies and Parameter Estimation 40
Towards the Combination of Data Sets from Various Observation Techniques 41
1 Introduction 41
2 Gauss-Markov Model 42
3 Combination Models 42
4 Numerical Example 44
5 Multi-scale Combination 46
Conclusion 48
References 48
On the Weighted Total Least Squares Solutions 50
1 Introduction 50
2 Mathematical Models of the TLS Problem 51
3 The Weighted TLS Solutions 51
3.1 Solutions Using Lagrange Multipliers 51
3.2 Solutions Using the GHM Method 52
3.3 Solutions Using the Gradient 53
4 Test Results 54
Conclusions and Further Work 54
Appendix 54
References 55
Integration of Observations and Models in a Consistent Least Squares Adjustment Model 56
1 Introduction 56
2 Integration in a Least Squares Adjustment Model 57
2.1 Functional Model 57
2.2 Stochastic Model 57
2.3 Linear Least Squares Adjustment 58
3 Example 58
Conclusions and Outlook 60
References 61
Comparison of Different Combination Strategies Applied for the Computation of Terrestrial Reference Frames and Geodetic Parameter Series 62
1 Introduction 62
2 Least Squares Adjustment by Gauß-Markov Model 63
3 Combination Strategies 64
3.1 Combination on Observation Level 65
3.2 Combination on Normal Equation Level 65
3.3 Combination on Parameter Level 66
4 Comparison of the Different Combination Strategies 66
5 Combination in the Geodetic Practice: The Realization of ITRS 67
Summary and Conclusions 68
References 69
W-Ratio Test as an Integer Aperture Estimator: Pull-in Regions and Ambiguity Validation Performance 70
1 Introduction 70
2 Parameter Estimation 71
3 W-Ratio Statistical Test 71
4 W-Ratio as an Integer Aperture Estimator 72
5 Numerical Analysis 73
Concluding Remarks 75
References 75
Performing 3D Similarity Transformation Using the Weighted Total Least-Squares Method 76
1 Introduction 76
2 Nonlinear 3D Similarity Transformation 77
3 The Nonlinear 3D Similarity Transformation Solved by the Solution Within the Nonlinear GH Model 78
4 Case Study 80
Concluding Remarks 81
References 82
Comparison of SpatialAnalyzer and Different Adjustment Programs 83
1 Introduction 83
2 Net Adjustment 84
2.1 SpatialAnalyzer 84
2.2 Network 84
2.3 Results 85
3 Form Fitting 85
Conclusion 87
References 88
Part III Recursive State-Space Filtering 89
State-Space Filtering with Respect to Data Imprecision and Fuzziness 90
1 Introduction 90
2 Modeling of Imprecision and Fuzziness 91
2.1 Sources of Uncertainty 91
2.2 Modeling of Uncertainty 92
3 Introduction to State-Space Filtering 93
3.1 System and Measurement Equation 93
3.2 Extended Kalman Filter 93
4 Imprecise Filter Extension 94
4.1 General Case 94
4.2 KF with Imprecise and Fuzzy Data 94
5 Example for Imprecise Kalman Filtering 95
Conclusions 97
References 97
Unscented Kalman Filter Algorithm with Colored Noise and Its Application in Spacecraft Attitude Estimation 98
1 Introduction 98
2 Mathematical Model of Satellite Attitude 99
2.1 Quaternion in Satellite Attitude 99
2.2 Nonlinear Model of the Satellite Attitude 99
3 UKF Algorithm 100
4 Series Representation of Colored Noise and Its Variance Calculation 101
5 Test Computation and Analysis 101
Concluding Remarks 102
References 103
Principles and Comparisons of Various Adaptively Robust Filters with Applications in Geodetic Positioning 104
1 Introduction 104
2 Principle of Adaptive Filtering 105
2.1 Principle of Fading Kalman Filter 105
2.2 Principle of Sage Windowing Filter 105
2.3 Principle of Robust Filter 105
2.4 Adaptively Robust Filter 106
3 Design of the Adaptive Factors 106
3.1 Fading Factors 106
3.2 Robust Weight Element 106
3.3 Adaptive Factors 106
4 Computations and Comparisons 106
Concluding Remarks 108
References 108
Alternative Nonlinear Filtering Techniques in Geodesy for Dual State and Adaptive Parameter Estimation 109
1 Introduction 109
2 Nonlinear State Estimation 110
2.1 The Probabilistic Inference 110
2.2 The Bayes Filter 111
2.3 The Extended Kalman Particle Filter 111
2.4 Combined Parameter and State Estimation in EKPF Algorithm 112
3 Numerical Applications 112
3.1 Tracking of a Nonlinear Trajectory 112
3.2 Tracking of a Nonlinear Trajectory 113
Conclusion 115
References 115
Part IV Sensor Networks and Multi Sensor Systems in Engineering Geodesy 116
Parametric Modeling of Static and Dynamic Processes in Engineering Geodesy 117
1 Introduction 117
2 Parametric Modeling of Deformation Processes 118
3 Case Study: The Mass Movement `Steinlehnen' 119
3.1 Monitoring System 119
3.2 Parametric Slope Model 119
4 Parametric Identification of a Simplified Scarp 121
4.1 Static Example 121
4.2 Dynamic Example 122
Conclusions 124
References 124
Land Subsidence in Mahyar Plain, Central Iran, Investigated Using EnvisatSAR Data 126
1 Introduction 126
2 Study Area 127
3 Methodology 127
4 Experimental Results and Analysis 127
4.1 InSAR Result 127
4.2 Earth Fissures 128
4.3 Piezometric Records 128
Conclusions 128
References 129
Recent Impacts of Sensor Network Technology on Engineering Geodesy 130
1 Introduction 130
2 Geo Sensor Network Technology 131
2.1 General Statements 131
2.2 Sensing 132
2.3 Communication 132
2.4 Network Operating 133
3 Selected Aspects 134
3.1 Calibration of Motes: An Example 134
3.2 Carrier Phase Based Positioning Using Simple Navigation Receivers 134
Conclusion and Outlook 136
References 136
Design of Artificial Neural Networks for Change-Point Detection 138
1 Introduction 138
2 Artificial Neural Networks 139
3 Change-Point Detection by ANN 140
4 Results and Practical Considerations 141
5 Summary and Outlook 142
References 143
Spatial and Temporal Kinematics of the Inylchek Glacier in Kyrgyzstan Derived from Landsat and ASTER Imagery 144
1 Introduction 144
2 Data and Methodology 145
2.1 ASTER Imagery 145
2.2 Landsat Imagery 146
3 Results 146
Conclusion 147
References 147
Response Automation in Geodetic Sensor Networks by Means of BayesianNetworks 149
1 Introduction 149
2 State Description 150
3 Bayesian Networks for the Assessment of Events 150
4 Soft Evidence by Kalman Filter 151
5 Simulation Results 152
Conclusion 153
References 154
Efficiency Optimization of Surveying Processes 155
1 Introduction 155
2 Process Modeling and Simulation 156
2.1 Elements of a Petri Net 156
2.2 Timed Petri Nets 156
2.3 Run of a Petri Net 156
3 Optimization 157
4 Time Model 157
5 Numerical Study and Results 158
6 Summary and Outlook 160
References 160
Modeling and Propagation of Quality Parameters in Engineering Geodesy Processes in Civil Engineering 161
1 Introduction 161
2 Quality Model 162
2.1 Fundamentals 162
2.2 Exemplary Parameters 162
3 Quality Propagation Methods 163
3.1 General 163
3.2 Standard Deviation 163
3.3 Tolerance Correctness 164
4 Simulation Results 164
4.1 Example: Basement Walls 164
4.2 Different Simulation Scenarios 164
Conclusions 166
References 166
Part V Multi-Mission Approaches with View to Physical Processes in the Earth System 167
Completion of Band-Limited Data Sets on the Sphere 168
1 Introduction 168
2 Stochastic Processes on the Sphere 169
3 Model Building and Separability 171
4 Application and Simulations 171
Summary and Conclusions 173
Appendix 173
Expectation and Variance of a Stochastic Process in Amplitude/Phase Notation on the Sphere 173
References 175
List of Reviewers 176
Author Index 177