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 lab 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.
- 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.
- ZIP file with examples from the lectures, answers to the tutorial problems, supplementary lecture notes, and files for the lab exercises.
- Instruction for computer lab 1 (time-of-arrival estimation).
- Instruction for computer lab 2 (music recognition lab).
- Previous exams with solutions
Video lectures with material from pre-requisite courses: (for registered students only, requires LiU-login)
- Recap of some concepts from linear algebra: vectors and matrices, norms, quadratic forms, eigen-decomposition, trace, determinant, and inverses
- Recap of orthogonal projections (from linear algebra)
- Recap of least squares (from linear algebra)
- About the Chi-square probability distribution
- 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 09 27 19:35