TSKS15 Detection and Estimation of Signals
TSKS15 Detection and Estimation of Signals treats statistical signal processing, specifically parameter estimation and detection of signals. The purpose of the course is to provide a solid foundation in algorithms, models, methods and theory for the extraction of information from noisy signals. Applications are found within radar systems, communications systems, positioning systems and image analysis.
- Problems in radar, communications and source localization systems.
- Classical versus Bayesian approaches.
- Hypothesis testing. Binary and M-ary tests. Bayes cost and error probability. Neyman-Pearson theorem.
- Classical estimation. Maximum-likelihood, Fisher information, Cramer-Rao bound.
- Bayesian estimation theory. MMSE and LMMSE.
- Composite hypothesis testning. GLRT. Marginalization. Model selection.
- Linear and nonlinear models with Gaussian noise. Slepian-Bang formula. Noise whitening.
- Detection of signals in continuous time.
- Performance and variance analysis. Asymptotic properties of estimates.
- Complex-valued data and noise. Circularly symmetric noise.
- Applications: amplitude, frequency, phase, time-delay and angle estimation.
The course consists of a lecture series and two computer projects:
- Radar range estimation. The purpose of this project will be to implement a maximum-likelihood time-of-arrival estimator, perform Monte-Carlo simulation of its performance, and compare with theoretical bounds.
- Music transcription. The purpose of this project will be to implement a Matlab routine that transcribes music, i.e., detects the corresponding notes and their duration from a recorded file.
- Course director and lecturer: Erik G. Larsson
- Tutorial assistant: Marcus Karlsson
- Lab assistants: Zheng Chen, Marcus Karlsson
- S. Kay, Statistical Signal Processing: Estimation Theory (Volume I) and Statistical Signal Processing: Detection Theory (Volume II), Prentice-Hall.
An offer has been negotiated with the on-campus bookshop, Bokab, 1029 SEK for a package consisting of both volumes.
We will cover selected material from Chapters 1-4, 6-12 and 15 in Volume I, and from Chapters 1-9 in Volume II.
The books are permitted on the exam (but without any additional notes).
- The first lecture is on August 30, at 1.15 pm, in room U11.
- The complete schedule is available from this link.
- The following laboratory sessions will be staffed with assistants: September 21, at 10.15, and October 4, at 15.15.
- The laboratory sessions on October 10 at 10.15 am (examination of radar lab), and October 13 at 8.15 am (examination of music lab) are compulsory.
We will arrange an extra examination session for students who have legitimate reasons for absence (e.g. collisions with other courses). If this applies, please contact the course director in due time.
Documents and files:
- Lecture and tutorial plan. (Will be updated during the course.)
- Slides and Matlab examples from the lectures, answers to tutorial problems, and supplementary notes. (ZIP file)
- Instruction for the radar lab. (Relevant theory will be covered in lectures 4-7.)
- Instruction for the music lab. (Relevant theory will be covered in lectures 1-11.)
- Data files for the music lab.
- Supplementary tutorial problems.
- Information about the written exam (cover sheet) is here.
More general information:
- Course description in the study guide
- Here is a poster from the department's open house with some pictures that illustrate the course themes.
Erik G. Larsson
Last updated: 2017 09 27 10:51