Recursive Estimation
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Description
Introduction to state estimation; probability review; Bayes theorem; Bayesian tracking; extracting estimates from probability distributions; Kalman filter; extended Kalman filter; particle filter; observer-based control and the separation principle.
Literature
Class notes and slides (will be available online).
Requirements
Introductory probability theory and matrix-vector algebra.
Announcements
Class Facts
Lectures
The lectures and recitations will be held in person in room HG F1. The lectures will be recorded. The recordings can be found in the Video Portal.
Recitation
Weekly recitations start on Feb 28. The recitations will be held in person in room HG F1, where the teaching assistants discuss and illustrate with examples topics from the previous week's lecture. The recitations will be recorded. The recordings can be found in the Video Portal.
Piazza Forum
Students are encouraged to post questions regarding the lectures and problem sets on the Piazza forum. We welcome everyone to try to answer and discuss about the questions posted.
Programming Exercise (optional)
During the semester, there will be a programming exercise, which requires the student to apply the course material. Though no bonus points will be given for the programming exercise, we strongly encourage the students to do it as it helps to better understand the content. Only submissions by individual students will be accepted and no teams are allowed. The programming exercise will be provided in Python.
For the top three submissions, we will issue prizes including signed certificates from professor D'Andrea, gift vouchers of 100 CHF, and a guided tour of Verity Studios.
Problem Sets
We will make sets of problems and solutions available online for the topics covered in the lecture. It is the student's responsibility to solve the problems and understand their solutions. The teaching assistants will answer questions in office hours and some of the problems might be covered during the recitations. The problem sets contain programming exercises that require the student to implement the lecture material in Python. In the final exam, there may be specific problems about the programming exercises.