ELEC-E8740 - Basics of sensor fusion D, Lecture, 6.9.2022-9.12.2022
This course space end date is set to 09.12.2022 Search Courses: ELEC-E8740
Topic outline
-
Tentative Schedule
6.9. Lecture 1: Course Overview and Introduction to Sensor Fusion
9.9. Exercise 1: Sensor Models13.9. Recap of matrix computations and Python
16.9. Matrix computations exercises in Python20.9. Lecture 2: Sensors, Models, and Least Squares Criterion (iPad Notes 2)
23.9. Exercise 2: Gaussian Distribution and Cost Functions27.9. Lecture 3: Static Linear Models and Linear Least Squares (iPad Notes 3)
30.9. Exercise 3: Linear Models and Least Squares4.10. Lecture 4: Static Nonlinear Models, Gradient Descent, and Gauss-Newton (iPad Notes 4)
7.10. Exercise 4: Nonlinear Optimization11.10. Lecture 5: Gauss-Newton with Line Search and Levenberg-Marquardt Algorithm (iPad Notes 5)
14.10. Exercise 5: Nonlinear Optimization IIMonday 17.10. at 9-12 First exam -- covers Lectures 1-5
25.10. Project work information session
28.10. No class1.11. Lecture 6: Linear Continuous-Time Dynamic Models (iPad Notes 6)
4.11. Exercise 6: Dynamic Models I8.11. Lecture 7: Nonlinear Continuous-Time Models and Discrete-Time Dynamic Models (iPad Notes 7)
11.11. Exercise 7: Dynamic Models II15.11. Lecture 8: Discretization of Continuous-Time Dynamic Models (iPad Notes 8)
18.11. Exercise 8: Dynamic Models III20.11. Project work part I DL
22.11. Lecture 9: Filtering Problem and Kalman Filtering (iPad Notes 9)
25.11. Exercise 9: Kalman filtering29.11. Lecture 10: Extended and Unscented Kalman Filtering
2.12. Exercise 10: Nonlinear Kalman filtering18.12. Project work part II DL
Friday 9.12. at 13-16 Second exam -- covers Lectures 6-10
Self-study material (if you want to do an extra homework): Lecture 11: Boostrap particle filtering
Homework 11 (Extra)