[ 481VMRSSSVK22 ] KV Statistical Signal Processing

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Workload Education level Study areas Responsible person Hours per week Coordinating university
3 ECTS M - Master's programme Mechatronics Mario Huemer 2 hpw Johannes Kepler University Linz
Detailed information
Original study plan Master's programme Mechatronics 2022W
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 Oral exam
Methods Theory presented by lecturer, Matlab based presentation
Language Upon agreement with participants – English or 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
Corresponding lecture (*)MEMWDVOSTSV: VO Statistische Signalverarbeitung (3 ECTS)
On-site course
Maximum number of participants 35
Assignment procedure Assignment according to sequence