MS1SZN - Navigation Systems

Course specification
Course title Navigation Systems
Acronym MS1SZN
Study programme Electrical Engineering and Computing
Module Signals and Systems
Type of study master academic studies
Lecturer (for classes)
    Lecturer/Associate (for practice)
      Lecturer/Associate (for OTC)
        ESPB 6.0 Status elective
        Condition Passed the subject: Automatic Guidance of Objects in Space - OS4AVO
        The goal Introduce students to the fundamentals of navigation system concepts and navigation algorithms for the cases of inertial navigation, radio navigation , and the navigation based on usage of video sensors Understanding of the nature of navigation process, physical sources of sensor errors, error propagation
        The outcome Understanding of nature of navigation process from the aspects of: available methods, sources of inaccuracies, choices of structure and elements of a system, and of application of optimal estimation for an accuracy improvement. Enabling students to produce the model of navigation systems.
        Contents of lectures Coordinate frames used in navigation and transformations between them. Fundamentals of inertial navigation – kinematic model, mechanization of a navigation algorithm, inertial sensors, sensor error modeling. Initialization and error estimation. Satellite global positioning system . Basics of machine vision, projective geometry, and dynamic vision. Methods of integration of navigation systems
        Contents of exercises Case studies: strap-down INS, GPS, integration of INS and GPS, robot control based on visual navigation. Individual realization of simulation models of navigation systems.
        1. S. Graovac: "Automatsko vođenje objekata u prostoru", Akademska misao, 2005. (Original title)
        2. J. A. Farrel, M. Barth: "The Global Positioning System & Inertial Navigation", McGraw-Hill, 1999. (Original title)
        3. A. Lawrence: "Modern Inertial Technology", Springer, 1998. (Original title)
        4. K. Kanatani: "Geometric Computation for Machine Vision", Clarendon Press, 1993. (Original title)
        Number of hours per week during the semester/trimester/year
        Lectures Exercises OTC Study and Research Other classes
        3 1
        Methods of teaching Lectures, exercises on computer, analysis of recommended literature, home-works, and projects.
        Knowledge score (maximum points 100)
        Pre obligations Points Final exam Points
        Activites during lectures 0 Test paper 20
        Practical lessons 20 Oral examination 20
        Projects 40
        Colloquia 0
        Seminars 0