[ 489INTEOASV17 ] VL Optimum and Adaptive Signal Processing Systems

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Workload Education level Study areas Responsible person Hours per week Coordinating university
3 ECTS M2 - Master's programme 2. year (*)Informationselektronik Mario Huemer 2 hpw Johannes Kepler University Linz
Detailed information
Original study plan Master's programme Electronics and Information Technology 2021W
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 Written or oral (depending on the number of subscribed students)
Methods Theory presented by lecturer, Matlab based presentations
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
Corresponding lecture (*)489WSIVOASV14: VL Optimale und Adaptive Signalverarbeitungssysteme (3 ECTS)
On-site course
Maximum number of participants -
Assignment procedure Assignment according to sequence