TSKS15 · 6 credits · Advanced level (A1N) · Elektroteknik
Detection and Estimation of Signals
Active · next start HT 2026
49%
passed the latest ordinary exam
TEN1 · 29 Oct 2025 · 35 students
60%
passed the ordinary exams, all sittings over the last 5 years
TEN1 · 153 students
Unusually hard in 2025/26
49% passed the ordinary exam in 2025/26 (35 students), against 64% on average in earlier years (118 students).
Part of 5 programmes
Exam
TEN1 Written examination
- 5
- 4
- 3
- U
Ordinary exams
60% passed · 153 students
- 49%35
- 61%28
- 51%35
- 72%29
- 73%26
Re-exams
68% passed · 31 students
- 83%12
- 63%8
- 100%6
- 0%5
- Pass the re-exam: 68% (31 students), compared with 60% (153 students) on the ordinary exam
- Get 4 or 5: 21% (184 students)
Show small sittings (6)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 75%4
- 0%4
- 67%3
- 25%4
- 0%2
- 100%1
Other modules
Partial exams, hand-ins and labs. They are not part of the figures at the top.
Hand-in or lab
LAB1 Laboratory work
- G
Main rounds
100% passed · 131 students
- 100%34
- 100%21
- 100%30
- 100%27
- 100%19
Later rounds
No sittings with at least 5 students.
Show small sittings (7)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 100%1
- 100%1
- 100%1
- 100%1
- 100%1
- 100%1
- 100%1
About the course
Aim
After completing the course, the student should be able to
- Understand and analyze fundamental detection and estimation concepts and limits, such as the receiver-operator characteristic and Cramer-Rao bound,
- Apply both classical and Bayesian detection and estimation methods to linear and nonlinear signal models, such as performing hypothesis tests,
- Describe, apply and implement parameter estimation via maximum-likelihood and detection using Bayesian methods in a conventional programming language. In general, the student should be able to demonstrate an engineering understanding of these topics.
Content
Binary hypothesis tests, Neyman-Pearson theorem, error probability. M-ary detection problems. Bayes cost, minimum probability of error. Nuisance parameters. Classical estimation: Maximum-likelihood. Cramer-Rao bound, Slepian-Bang's formula, efficiency. Linear, vector-valued models with noise. Non-linear models. Noise whitening, Gaussian noise. Bayesian estimation: MMSE and LMMSE. Composite hypothesis testing: GLRT and Bayesian approach. Performance calculations, asymptotic properties of estimators. Applications in radar and communications technology: amplitude and phase estimation, frequency estimation, angle-of-arrival estimation, time-of-arrival estimation, source localization, coherent and non-coherent detection of waveforms.
- Examiner
- Karl-Ludwig Besser
- Scheduled hours
- 44 h
- Main field of study
- Elektroteknik