[ 489INTEOASU17 ] UE Optimum and Adaptive Signal Processing Systems

Es ist eine neuere Version 2023W dieser LV im Curriculum Master's programme Mechatronics 2023W vorhanden.
Workload Education level Study areas Responsible person Hours per week Coordinating university
1,5 ECTS M2 - Master's programme 2. year (*)Informationselektronik Mario Huemer 1 hpw Johannes Kepler University Linz
Detailed information
Original study plan Master's programme Electronics and Information Technology 2017W
Objectives Design of optimum estimation algorithms for signal processing problems, Design of optimum and adaptive filters, Design of Kalman filters.
  • Design of optimum estimation algorithms (CRLB, MVU, BLUE, LS, MMSE, LMMSE, MAP; Applications: amplitude estimation, frequency estimation, power estimation, signal extraction, system identification)
  • Optimum filters (Wiener filter; Least squares filter; Applications: system identification, inverse system identification, noise cancellation, linear prediction)
  • Adaptive filters (LMS algorithm; RLS algorithm)
  • Kalman filter (Kalman filter for linear systems; Extended Kalman filter for non-linear systems)
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 German
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 Language can be switched to English if requested

Until term 2017S known as: 489WSIVOASU14 UE Optimum and Adaptive Signal Processing Systems
Earlier variants They also cover the requirements of the curriculum (from - to)
489WSIVOASU14: UE Optimum and Adaptive Signal Processing Systems (2014W-2017S)
On-site course
Maximum number of participants 35
Assignment procedure Assignment according to sequence