Inhalt
[ 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) 2023W 
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 indepth problems and generalize the results obtained.

Subject 
 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 
Earlier variants 
They also cover the requirements of the curriculum (from  to) 489INTEOASU17: UE Optimum and Adaptive Signal Processing Systems (2017W2022S) 489WSIVOASU14: UE Optimum and Adaptive Signal Processing Systems (2014W2017S)




Onsite course 
Maximum number of participants 
35 
Assignment procedure 
Assignment according to sequence 


