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

Part I Uncertainty modeling of geodetic data.- 1. Modeling Data Quality Using Artificial Neural Networks.- 2. Magic Square of Real Spectral and Time Series Analysis with an Application to Moving Average Processes.- 3. Describing the Quality of Inequality Constrained Estimates.- 4. GNSS Integer Ambiguity Validation Procedures: Sensitivity Analysis.- 6. Optimal Design of Deformation Monitoring Networks Using the Global Optimization Methods.- Part II Theoretical Studies on Combination Strategies and Parameter Estimation.- 7. Towards the Combination of Data Sets from Various Observation Techniques.- 8. On the Weighted Total Least Squares Solutions.- 9. Integration of Observations and Models in a Consistent Least Squares Adjustment Model.- 10. Comparison of Different Combination Strategies Applied for the Computation of Terrestrial Reference Frames and Geodetic Parameter Series.- 11. W-Ratio Test as an Integer Aperture Estimator: Pull-in Regions and Ambiguity Validation Performance.- 12. Performing 3D Similarity Transformation Using the Weighted Total Least-Squares Method.- 13. Comparison of Spatial Analyzer and Different Adjustment Programs. Part III Recursive State-Space Filtering.- 14. State-Space Filtering With Respect to Data Imprecision and Fuzziness.- 15. Unscented Kalman Filter Algorithm with Colored Noise and its Application in Spacecraft Attitude Estimation.- 16. Principles and Comparisons of Various Adaptively Robust Filters with Applications in Geodetic Positioning.- 17. Alternative Nonlinear Filtering Techniques in Geodesy for Dual State and Adaptive Parameter Estimation.- Part IV Sensor Networks and Multi Sensor Systems in Engineering Geodesy.- 18. Parametric Modeling of Static and Dynamic Processes in Engineering Geodesy.- 19. Land subsidence in Mahyar plain, Central Iran, studied using SBAS-InSAR Method. 20. Recent Impacts of Sensor Network Technology on Engineering Geodesy.- 21. Design of Artificial Neural Networks for Change-Point Detection. 22. Spatial