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.
- 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, 92 GBP for both volumes.
We will cover Chapters 1-4, 6-12 and 15 from Volume I, and Chapters 1-9 from Volume II.
- 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 04 04 15:40