[ 489MAITOASU22 ] UE Optimum and Adaptive Signal Processing Systems

Workload Education level Study areas Responsible person Hours per week Coordinating university
1,5 ECTS M1 - Master's programme 1. year (*)Informationselektronik Mario Huemer 1 hpw Johannes Kepler University Linz
Detailed information
Original study plan Master's programme Electronics and Information Technology (ELIT) 2022W
Objectives Students know and understand the fundamental parameter estimation methods, the basics of optimum filters, adaptive filters and Kalman filters qualitatively and mathematically and can apply them to in-depth problems and generalize the results obtained.
  • Parameter Estimation
    • Classical Methods: MVU, BLUE, ML, LS
    • Bayesian Methods: MAP, MMSE, LMMSE
    • Applications: amplitude estimation, frequency estimation, power estimation, signal extraction, system identification, data estimation
  • Optimum Filters
    • Wiener Filters
    • Least Squares Filters
    • Applications: system identification (channel estimation), inverse system identification (e.g. for channel equalization), noise reduction, linear prediction (e.g. for voice signals)
  • Adaptive Filters
    • LMS (Least Mean Squares) algorithm
    • RLS (Recursive Least Squares) algorithm
  • Kalman Filters
    • Standard Kalman Filter
    • Extended Kalman Filter
    • Applications
Criteria for evaluation Home exercises have to be handed in during the semester, short oral final interview
Methods Examples presented by lecturer, home exercises
Language English
Study material
  • Lecture Slides
  • S. Kay, Fundamentals of Statistical Signal Processing: Estimation Theory, Prentice Hall, Rhode Island 1993.
  • D.G. Manolakis, V.K. Ingle, S.M. Kogon, Statistical and Adaptive Signal Processing, Artech House, 2005.
Changing subject? No
Further information Until term 2022S known as: 489INTEOASU17 UE Optimum and Adaptive Signal Processing Systems
until term 2017S known as: 489WSIVOASU14 UE Optimum and Adaptive Signal Processing Systems
On-site course
Maximum number of participants 35
Assignment procedure Assignment according to sequence