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.
The course is offered next time in the first quarter of the 2018 fall semester.
- 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:
- Time-of-arrival (range) estimation. The purpose of this project is to implement a maximum-likelihood time-of-arrival (range) estimation algorithm, perform Monte-Carlo simulation of its performance, and compare with theoretical bounds.
- Music recognition. The purpose of this project is to design a classifier algorithm for the recognition of music, implement this algorithm, and evaluate its performance and robustness to model assumptions using Monte-Carlo simulation.
- Course director and lecturer: Erik G. Larsson
- Tutorial assistant: TBD
- Lab assistants: TBD
- S. Kay, Statistical Signal Processing: Estimation Theory (Volume I) and Statistical Signal Processing: Detection Theory (Volume II), Prentice-Hall.
We will cover selected material from Chapters 1-4, 6-12 and 15 in Volume I, and from Chapters 1-9 in Volume II.
Documents and files:
- Lecture and tutorial plan (tentative).
- Examples from the lectures, answers to tutorial problems, and supplementary notes. (ZIP file from 2017, will be updated for 2018)
- Instruction for the time-of-arrival estimation lab.
- Instruction for the music recognition lab. (Re-designed for the 2018 edition of the course.)
- Previous exams with solutions
- Course description in the study guide
- Poster from the department's open house with some pictures that illustrate the course themes
- Short video with information about the course
Erik G. Larsson
Last updated: 2018 04 06 22:26