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2024/2025
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Welcome page > Faculty of Information Technology > Department of Applied Mathematics > Statistical Modelling Lab

Statistical Modelling Lab

The course is not on the list Without time-table
Code Completion Credits Range Language
NI-LSM KZ 5 3C Czech
Garant předmětu:
Lecturer:
Tutor:
Supervisor:
Department of Applied Mathematics
Synopsis:

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).

Requirements:

BI-LIN, BI-ZMA

Ideally BI-PST too.

Syllabus of lectures:

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

Syllabus of tutorials:

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

Study Objective:
Study materials:

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.

Note:
Further information:
https://courses.fit.cvut.cz/NI-LSM/
No time-table has been prepared for this course
The course is a part of the following study plans:
Data valid to 2024-04-26
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