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.

Course topics

• 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.
• Performance and variance analysis. Asymptotic properties of estimates.
• Applications: amplitude, frequency, phase, time-delay and angle estimation.

Instructors, fall 2024

• Course director and lecturer: Erik G. Larsson
• Assistants: Jianan Bai (tutorials and labs), Sai Subramanyam Thoota (lab examination)

Course material

• Required reading
  • Steven M. Kay, Fundamentals of Statistical Signal Processing –Volume I, Estimation Theory.
  • Steven M. Kay, Fundamentals of Statistical Signal Processing –Volume II, Detection Theory.
  • Supplementary notes by E. G. Larsson
• Supplementary material: PowerPoint slides, answers to the tutorial problems, and material for the labs.

Prerequisites

• Linear algebra, probability theory, and general mathematical maturity.
• Programming skills.

Information for the fall 2024 edition

For detailed lecture, tutorial and lab plans, please go here