Further Development of the L2/L1-norm GOCA Kalman-Filtering DLL and Extension to the Computation and Visualization of Variance Estimations and Probability and Forecasting States

Thèse de Master , 2006 , 101 Pages , Note: 1.3

Sciences de la Terre / Géographie - Cartographie, Géodésie, Sciences de l'information géographique

As further development of GOCA- (GNSS/LPS/LS-based Online Control and Alarm Systems) software, the Kalman filter was developed as additional module to monitor besides pure object point displacement also the velocity and the acceleration in a specified time interval. In this Master thesis the Kalman filter algorithm is modified, and additional capabilities are added. The additional capabilities include; first, a forecasting of expected displacement, velocity and acceleration to future. Second, computing the time at which the point displacement and velocity is expected to exceed the given critical values.
Two estimation algorithms are used in the GOCA-Kalman filtering; first, least squares adjustment (L2-norm estimation). Second, L1-estimation. Data analysis of given projects were to be carried out and compared using both adjustment algorithms.
To design and develop the GOCA-Kalman filter four steps are applied; first step, the GOCA-Kalman filter is realized and tested using MATLAB to create the mathematical algorithm and test the results of standard point given displacement, e.g. constant velocity displacement , parabola displacement, etc . Second step, a VC++ dynamic link library (.dll) is created. Third step, the DLL file was embedded in the GOCA software by calling the DLL file and its related libraries. And forth step, the Kalman filter graphics part had to be modified to show the state vector components (displacement, velocity, and acceleration) with their standard deviations, and additional the forecasted value and its standard deviation would be shown in the graphics part.
Additional work is added to this master thesis to make artificial displacement GKA-files (GNSS/LPS/LS input files in the GOCA-software), where points displacements with linear, parabola etc are created. The software was realized using MATLAB GUI and named GKA-create.

Inhaltsverzeichnis

• Objectives
• Introduction
• The GOCA-Kalman Filter
• The GOCA-Kalman Filter Algorithm
• The GOCA-Kalman Filter DLL
• The GOCA-Kalman Filter Graphics
• The GOCA-Kalman Filter Test
• The GOCA-Kalman Filter Results
• The GOCA-Kalman Filter Conclusion
• Bibliography

Zielsetzung und Themenschwerpunkte

This Master thesis focuses on the development and implementation of a Kalman filter module within the GOCA (GNSS/LPS/LS-based Online Control and Alarm Systems) software. The Kalman filter is designed to monitor not only object point displacement but also velocity and acceleration within a specified time interval. The thesis aims to enhance the Kalman filter algorithm by adding capabilities for forecasting future displacement, velocity, and acceleration, as well as calculating the time at which displacement and velocity are expected to exceed given critical values. The research involves comparing the performance of two estimation algorithms: least squares adjustment (L2-norm estimation) and L1-estimation, using real-world project data.

• Development and implementation of a Kalman filter module within the GOCA software.
• Enhancement of the Kalman filter algorithm to include forecasting capabilities.
• Comparison of the performance of least squares adjustment (L2-norm estimation) and L1-estimation algorithms.
• Analysis of real-world project data using the developed Kalman filter.
• Evaluation of the effectiveness of the Kalman filter in monitoring object point displacement, velocity, and acceleration.

Zusammenfassung der Kapitel

• Objectives: This chapter outlines the objectives and scope of the Master thesis, focusing on the development and implementation of a Kalman filter module within the GOCA software.
• Introduction: This chapter provides an overview of the GOCA system, its applications, and the need for a Kalman filter module to enhance its capabilities.
• The GOCA-Kalman Filter: This chapter delves into the theoretical foundation of the Kalman filter and its application within the GOCA system. It explains the principles of Kalman filtering and its role in monitoring object point displacement, velocity, and acceleration.
• The GOCA-Kalman Filter Algorithm: This chapter presents the detailed algorithm used for the GOCA-Kalman filter, including the mathematical equations and implementation details. It explains the steps involved in processing data and generating estimates of displacement, velocity, and acceleration.
• The GOCA-Kalman Filter DLL: This chapter discusses the development of a dynamic link library (DLL) for the GOCA-Kalman filter, which allows for its integration into the GOCA software. It explains the design and implementation of the DLL and its interaction with the GOCA system.
• The GOCA-Kalman Filter Graphics: This chapter focuses on the graphical representation of the Kalman filter results, including the visualization of displacement, velocity, and acceleration estimates, as well as their standard deviations. It explains the design and implementation of the graphical interface.
• The GOCA-Kalman Filter Test: This chapter describes the testing process for the GOCA-Kalman filter, including the use of simulated data and real-world project data. It explains the methods used to evaluate the performance of the Kalman filter and its accuracy in estimating displacement, velocity, and acceleration.
• The GOCA-Kalman Filter Results: This chapter presents the results of the testing process, including the accuracy of the Kalman filter estimates and the comparison of the performance of the two estimation algorithms (L2-norm and L1-estimation). It analyzes the results and draws conclusions about the effectiveness of the Kalman filter.

Schlüsselwörter

The keywords and focus themes of the text include Kalman filtering, GNSS/GPS/LPS-based Online Control and Alarm Systems (GOCA), deformation monitoring, displacement, velocity, acceleration, forecasting, least squares adjustment (L2-norm estimation), L1-estimation, data analysis, and software development.

• Université: University of Applied Sciences Karlsruhe (Geomatik)
• Cours: Master of Geomatics
• Note: 1.3
• Auteur: Ghadi Younis (Auteur)
• Année de publication: 2006
• Pages: 101
• N° de catalogue: V276794
• ISBN (ebook): 9783656701941
• ISBN (Livre): 9783656703839
• Langue: anglais
• mots-clé: further, development, l2/l1-norm, goca, kalman-filtering, extension, computation, visualization, variance, estimations, probability, forecasting, states