Welcome to Principles of Signal Estimation, autumn 2026. Lectures run on Tuesdays at 12-14 in Hall B2, exercise sessions on Fridays at 12-14 in Lab 4. Teaching opens with the lecture of Tuesday 8.9.2026; the Friday of that same week, 11.9.2026, is given over to a recap of programming and matrix algebra, also in Lab 4.
Office hours: message the course chat to agree a slot, in person or online.
Lectures and Exercises
Main lecturer Prof. Antti Ranta
Homeworks and Exercises
Co-lecturer: Elena Fiore
Project Work
Nils Berg
Course chat
Discussion outside the sessions happens on the course chat server: https://coursechat.example.com/signal-estimation-2026
Intended Learning Outcomes
By the end of the course a participant should be able to:
- describe the building blocks of an estimation system and how they fit together,
- move from a physical description — differential equations, recursions, the behaviour of the sensors themselves — to a state space model in continuous or discrete time,
- recognise where a model stops being linear and explain what that costs an estimator
- assemble a recursive estimator for a given problem and argue why one formulation of it beats another.
Assessment Methods and Criteria
Whether those outcomes have been reached is judged from two written mid-term exams, the homework rounds and the project work. Points accumulate across the components listed below, and the figures given are the maximum each one can yield:
- Exam 1: 25 points
- Exam 2: 25 points
- Homeworks: 24 points (8 rounds, 3 points each)
- Project work part 1: 16 points
- Project work part 2: 20 points
That comes to 110 points in all, and the grade boundaries are:
- ≥92pts ↔ grade 5
- ≥79pts ↔ grade 4
- ≥66pts ↔ grade 3
- ≥55pts ↔ grade 2
- ≥45pts ↔ grade 1
One extra point comes from filling in the course feedback questionnaire, and if the timetable allows a bonus homework round worth up to 3 points is opened late in the term.
Worked through with numbers: 18 from Exam 1, 17 from Exam 2, 19 across the homework rounds, then 12 and 14 from the two project parts, plus the feedback point — 18 + 17 + 19 + 12 + 14 + 1= 81 points, which is a grade 4.
A retake covering the whole course is also arranged; its result substitutes for the 50 points of Exam 1 and Exam 2 together.
Study Material
There is no set textbook. The Reading materials section collects the notes and handouts each lecture builds on, posted as the term goes along.
Prerequisites
Linear algebra, probability and calculus are assumed. Earlier exposure to signals and systems, to estimation theory or to sensor hardware makes the term easier, but none of it is a formal requirement.
Not sure whether your background is enough? Come along to the recap session.