Adaptive and Array Signal Processing  Modultafeln of TU Ilmenau
The Modultafeln have a pure informational character. The legally binding information can be found in the corresponding Studienplan and Modulhandbuch, which are served on the pages of the course offers. Please also pay attention to this legal advice (german only). Information on place and time of the actual lectures is served in the Vorlesungsverzeichnis.
subject properties Adaptive and Array Signal Processing in major Master Elektrotechnik und Informationstechnik 2014 (IKT)  

subject number  5848 
examination number  2100218 
department  Department of Electrical Engineering and Information Technology 
ID of group  2111 (Communications Engineering Group) 
subject leader  Prof. Dr. Martin Haardt 
term  Wintersemester 
language  Englisch 
credit points  5 
oncampus program (h)  45 
selfstudy (h)  105 
Obligation  obligatory 
exam  written examination performance, 120 minutes 
details of the certificate  
maximum number of participants  
previous knowledge and experience  Bachelor 
learning outcome  The fundamental concepts of adaptive filters and array signal processing are developed in class. The students understand the relationships between temporal and spatial filters, as well as the principle of highresolution parameter estimation, and they are able to adapt their knowledge to other scientific disciplines. The students are able to develop or improve algorithms and to evaluate their performance in an analytical manner or by simulations. Futhermore, the students are enabled to read and understand current research publications in the areas of adaptive filters and array signal processing and they can use these concepts and results for their own research. 
content  1 Introduction 2.1 Calculus 2.2 Stochastic processes 2.3 Linear algebra 3 Adaptive Filters 3.2 Linearly Constrained Minimum Variance Filter 3.3 Generalized Sidelobe Canceler 3.4 Iterative Solution of the Normal Equations 3.5 Least Mean Square (LMS) Algorithm 3.6 Recursive Least Squares (RLS) Algorithm 4.1 Spectral MUSIC 4.2 Standard ESPRIT 4.3 Signal Reconstruction 4.4 Spatial smoothing 4.5 Forwardbackward averaging 4.6 Realvalued subspace estimation 4.7 1D Unitary ESPRIT 4.8 Multidimensional Extensions 4.9 Multidimensional RealTime Channel Sounding 4.10 Direction of Arrival Estimation with Hexagonal ESPAR Arrays 5.1 Introduction and Motivation 5.2 Fundamental Concepts of Tensor Algebra 5.3 Elementary Tensor Decompositions 5.4 Tensors in Selected Signal Processing Applications 6 Maximum Likelihood Estimators 6.1 Maximum Likelihood Principle 6.2 The Fisher Information Matrix and the Cramer Rao Lower Bound (CRLB) 
media of instruction  Skript, Overheadprojektor, Beamer Script, projector 
literature / references 

evaluation of teaching  Pflichtevaluation: WS 2011/12 (Fach) Freiwillige Evaluation: WS 2008/09 (Vorlesung) WS 2010/11 (Vorlesung) WS 2012/13 (Vorlesung) WS 2013/14 (Vorlesung) Hospitation: WS 2011/12 