A course is the basic teaching unit, it's design as a medium for a student to acquire comprehensive knowledge and skills indispensable in the given field. A course guarantor is responsible for the factual content of the course.
For each course, there is a department responsible for the course organisation. A person responsible for timetabling for a given department sets a time schedule of teaching and for each class, s/he assigns an instructor and/or an examiner.
Expected time consumption of the course is expressed by a course attribute extent of teaching. For example, extent = 2 +2 indicates two teaching hours of lectures and two teaching hours of seminar (lab) per week.
At the end of each semester, the course instructor has to evaluate the extent to which a student has acquired the expected knowledge and skills. The type of this evaluation is indicated by the attribute completion. So, a course can be completed by just an assessment ('pouze zápočet'), by a graded assessment ('klasifikovaný zápočet'), or by just an examination ('pouze zkouška') or by an assessment and examination ('zápočet a zkouška') .
The difficulty of a given course is evaluated by the amount of ECTS credits.
The course is in session (cf. teaching is going on) during a semester. Each course is offered either in the winter ('zimní') or summer ('letní') semester of an academic year. Exceptionally, a course might be offered in both semesters.
The subject matter of a course is described in various texts.
NI-LSM Statistical Modelling Lab Extent of teaching: 3C Instructor: Dedecius K. Completion: KZ Department: 18105 Credits: 5 Semester: L Annotation:
The subject is oriented on a single and multi-target tracking. The student both learns the existing methods and tries to implement them. The stress is put on the effective use of the available information and its modeling using numpy and scipy. The second half of the semester is focused on the design of methods and algorithms, and analyses of their properties. At this point, the subject is on the border of own research and may result in the topic of final work (diploma or bachelor thesis).
Lecture syllabus:
1. Introduction into statistical modelling, Bayesian approach. 2. Linear model, prior and posterior information. 3. Kalman filter, single target tracking. 4. Kalman filtering in clutter. 5. PDA filter. 6. PDA filtering continued. 7. Project: Assignment. 8. Project: Analysis of the state of the art. 9. Project: Design of suitable solutions. 10. Project: Implementation of proposed solutions. 11. Project: Analysis of results. 12. Project: Assessment Seminar syllabus:
1. Introduction into statistical modelling, Bayesian approach. 2. Linear model, prior and posterior information. 3. Kalman filter, single target tracking. 4. Kalman filtering in clutter. 5. PDA filter. 6. PDA filtering continued. 7. Project: Assignment. 8. Project: Analysis of the state of the art. 9. Project: Design of suitable solutions. 10. Project: Implementation of proposed solutions. 11. Project: Analysis of results. 12. Project: Assessment Literature:
1. E. Brekke: Fundamentals of sensor fusion. NTNU, 2021 2. X. Rong Li and Y. Bar-Shalom, ?Tracking in clutter with nearest neighbor filters: analysis and performance,? IEEE Transactions on Aerospace and Electronic Systems, vol. 32, no. 3, pp. 995?1010, Jul. 1996, doi: 10.1109/7.532259. 3. Y. Bar-shalom, F. Daum, and J. I. M. Huang, ?The probabilistic data association filter,? IEEE Control Systems, vol. 29, no. 6, pp. 82?100, Dec. 2009, doi: 10.1109/MCS.2009.934469. Requirements:
BI-LIN, BI-ZMA Ideally BI-PST too.
Informace o předmětu a výukové materiály naleznete na https://courses.fit.cvut.cz/NI-LSM/ The course is also part of the following Study plans:
Page updated 20. 4. 2024, semester: L/2023-4, L/2020-1, L/2022-3, L/2021-2, Z/2019-20, Z/2022-3, Z/2020-1, Z/2023-4, L/2019-20, Z/2021-2, Z/2024-5, Send comments to the content presented here to Administrator of study plans Design and implementation: J. Novák, I. Halaška