Preliminary Lecture Plan

The times for the lectures are preliminary and up for discussion to minimize overlap with other courses.

Slides will be linked from the lecture number in advance. For now, the slides from last version of the course are available to give a hint about the lecture content, but expect them to change shortly before the actual lecture is given.

Recomended reading refers to the textbook Linear Estimation.

Nr.	When	Where	Content	Slides	Reading
1.	2023-09-19 15--17	Big conference room in Visionen	Least-squares (LS) estimation and the conditional mean. Linear LS estimation and the special case of Gaussian random variables.	1-up 4-up notes	Sec. 3.1-3.2.4 article [1]
2.	2023-10-03 10-12	Big conference room in Visionen	Geometric interpretation of linear LS estimation. Spectral factorization. Discrete time causal Wienerfilters.	1-up 4-up notes	Sec. 3.3 (Bonus: Sec. 3.4-3.5) Sec. 6.3-6.5 Sec. 7.3-7.7
3.	2023-10-17 10-12	Big conference room in Visionen	Bayes: Estimating an point or a density. State-space models and Markov process. Recursive Bayesian filtering. Linear Gaussian models and the Kalman filter.	notes	Lecture notes only
4.	2023-11-06 10-12	Big conference room in Visionen	The innovation process. The Kalman filter from an innovation process perspective.	notes	Sec. 4.1-4.2.4 Sec. 9.1-9.4
5.	2023-11-21 13-15	Big conference room in Visionen	Observability and controllability. Time Invariance of the Kalman Filter. Frequency Domain Expressions.	notes	Sec. 1.5 Sec. 14.1-14.3
6.	2023-12-07 13-15	Big conference room in Visionen	Smoothing filters. Information Form Kalman Filter. Extended Kalman Filter (EKF).	notes	Sec. 9.5-9.9 Sec. 10.1-10.4