Kinaematic and Dynamic Positioning

Mode of Delivery

Face to Face

Prerequisites and Co-Requisites

None

Recommended Optional Programme Components

Geodetic Parameters Estimation and Linear Hypothesis Testing Analysis and Evaluation of GNNS Data Monitoring of Crustal Movements using GPS/GNNS Measurement Techniques Monitoring of Deformation Networks

Course Contents

Introduction, kinematic and dynamic modeling, the advantages and disadvantages. Measurement model. Modeling of geodetic measurements. Linear and nonlinear dynamic systems. Transition matrix, controllability and measurability. Optimal prediction. Stochastic structure of the dynamic and measurement models, the noise. Linear filtering and smoothing. Kalman filter, nonlinear approaches. Examples from kinematics positioning. Innovation, gain matrix, sensitivity analysis and implementation with examples of geodesic problem. Global and local tests. Adaptive and extended Kalman filter. Kalman filter implementation of the geodesic problem.

Weekly Schedule

1) Introduction. Kinematic and dynamic modelling. Advantanges and disadvantanges. Measurement model. Modelling of geodetic observations.
2) Linear and unlinear dynamic systems.
3) Transition matrix.
4) Optimal pretiction.
5) Stochastic structures of dynamic and measurement models.
6) Noise, white noise, coluered noise.
7) Linear filter and smoothing. Kalman Filter. nonlinear approach.
8) Midterm examination/Assessment
9) Kalman Filter and Least Squared Method.
10) Samples for kinematic positining. Discrete approaches for geodetic problems.
11) Innovation, gain matrix, sensitivity analyses and their applications for geodesic problems.
12) Global and local tests.
13) Global and local tests.
14) Kalman Filter for geodetic problems.
15) Kalman Filter for geodetic problems.
16) Final examination

Recommended or Required Reading

1- Dinamik Sistemlerin GPS ve Kalman Filtresiyle Anlık Olarak İzlenmesi, Cankut D. İNCE, 1999, İTÜ, Phd. Tezi
2- Düşey Yöndeki Yerkabuğu Deformasyonlarının Kinematik Model ile Belirlenmesi, 1994, KTÜ, Phd. Tezi.
3- - M.S. Grewal and A.P. Andrews (1993). Kalman Filtering Theory and Practice. Prentice Hall, Englewood Cliffs, New Jersey.
4- - P.S. Maybeck (1979). Stochastic Models, Estimation and Control. Vol. 1, Academic Press, Inc. New York.
5- - R. G. Brown and P. Y.C. Hwang (1992). Introduction to Random Signals & applied Kalman Filtering. John Wiley & Sons, Inc.
6- - Koch, K. R., (1999), Parameter Estimation and Hypothesis Testing in Linear Models, Springer, New York.
7- - Moritz H., and Sünkel H. (1978) Approximation Methods in Geodesy, Herbert Wichmann Verlag Karlsruhe, Germany, 1978.
8- - Luca Bagnaschi (1993) Application of an Adaptive Kalman Fitler fort he Estimation of Position, Velocity and Acceleration of a Moving from GPS Measurments, IGP-ETH, IGP Bericht Nr.226, Zürich.
9- - Cross, P. A., (1983), Advanced Least Squares Applied to Positioning-Fixing, North East London Polytechnic-Department of Land Surveying, London-England.

Planned Learning Activities and Teaching Methods

1) Lecture
2) Question-Answer
3) Discussion
4) Drill and Practice
5) Modelling
6) Brain Storming
7) Case Study
8) Self Study
9) Problem Solving
10) Project Based Learning

Assessment Methods and Criteria

Contribution of Quiz to Course Grade	50%
Contribution of Final Examination to Course Grade	50%
Total	100%

Language of Instruction

Turkish

Work Placement(s)

Not Required