Technische Universität Ilmenau

Adaptive and Array Signal Processing, Part 1 - Modultafeln of TU Ilmenau

The module lists provide information on the degree programmes offered by the TU Ilmenau.

Please refer to the respective study and examination rules and regulations for the legally binding curricula (Annex Curriculum).

You can find all details on planned lectures and classes in the electronic university catalogue.

Information and guidance on the maintenance of module descriptions by the module officers are provided at Module maintenance.

Please send information on missing or incorrect module descriptions directly to modulkatalog@tu-ilmenau.de.

module properties Adaptive and Array Signal Processing, Part 1 in degree program Diplom Elektrotechnik und Informationstechnik 2017
module number200506
examination number2100840
departmentDepartment of Electrical Engineering and Information Technology
ID of group 2111 (Communications Engineering)
module leaderProf. Dr. Martin Haardt
term winter term only
languageEnglisch
credit points5
on-campus program (h)45
self-study (h)105
obligationelective module
examwritten examination performance, 75 minutes
details of the certificate
alternative examination performance due to COVID-19 regulations incl. technical requirements
signup details for alternative examinations
maximum number of participants
previous knowledge and experience
learning outcome

After completing this module, the students are able to understand the fundamental concepts of adaptive filters and array signal processing. These concepts include the mathematical background, in particular concepts and "tricks" that can be used for the derivation of new research results. Furthermore, they range from adaptive temporal and spatial filters to (multi-dimensional) high-resolution parameter estimation techniques and tensor-based signal processing concepts. The students have a deep understanding of these universal (timeless) principles that are applicable in several research areas and disciplines.

The students are enabled to read and understand current research publications in the areas of adaptive filters and array signal processing. They are able use these concepts and results for their own research and understand the presentations about these topics at international conferences. Furthermore, they are able to read and understand current IEEE journal and conference publications in this area. Moreover, they have been enabled to develop new research ideas and results that build on this published "state-of-the-art."

content

1Introduction
- Adaptive Filters  
- Single channel adaptive equalization (temporal filter)  
- Multi channel adaptive beamforming (spatial filter)

2 Mathematical Background
2.1 Calculus  
- Gradients 
- Differentiation with respect to a complex vector 
- Quadratic optimization with linear constraints (method of Lagrangian multipliers) 
2.2 Stochastic processes  
- Stationary processes 
- Time averages  
- Ergodic processes  
- Correlation matrices 2.3Linear algebra  
- Eigenvalue decomposition 
- Eigenfilter  
- Linear system of equations 
- Four fundamental subspaces 
- Singular value decomposition 
- Generalized inverse of a matrix 
- Projections 
- Low rank modeling

3 Adaptive Filters 
3.1 Linear Optimum Filtering (Wiener Filters)  
- Principle of Orthogonality  
- Wiener-Hopf equations  
- Error-performance surface 
- MMSE (minimum mean-squared error) 
- Canonical form of the error-performance surface 
- MMSE filtering in case of linear Models 
3.2 Linearly Constrained Minimum Variance Filter  
- LCMV beamformer  
- Minimum Variance Distortionless Response (MVDR) spectrum: Capon's method  
- LCMV beamforming with multiple linear constraints 
3.3 Generalized Sidelobe Canceler 
3.4 Iterative Solution of the Normal Equations 
- Steepest descent algorithm  
- Stability of the algorithm  
- Optimization of the step-size
3.5 Least Mean Square (LMS) Algorithm 
3.6 Recursive Least Squares (RLS) Algorithm

4 High-Resolution Parameter Estimation  
- Data model (DOA estimation)  
- Eigendecomposition of the spatial correlation matrix at the receive array  
- Subspace estimates  
- Estimation of the model order
4.1 Spectral MUSIC 
- DOA estimation  
- Example: uniform linear array (ULA) 
- Root-MUSIC for ULAs 
- Periodogram 
- MVDR spatial spectrum estimation (review)
4.2 Standard ESPRIT 
- Selection matrices 
- Shift invariance property
4.3 Signal Reconstruction 
- LS solution 
- MVDR / BLUE solution  
- Wiener solution (MMSE solution) 
- Antenna patterns 
4.4 Spatial smoothing

media of instruction and technical requirements for education and examination in case of online participation

Skript, Overheadprojektor, Beamer Script, projector

literature / references
  • T. Kaiser, A. Bourdoux, H. Boche, Smart Antennas State of The Art.
  • Hindawi Publishing Corporation, 2005.
  • A. H. Sayed, Fundamentals of Adaptive Filtering.
    John Wiley & Sons, Inc., New York, NY, 2003.
  • T. K. Moon and W. C. Stirling, Mathematical Methods and Algorithms for Signal Processing.
    Prentice-Hall, 2000.
  • S. Haykin and M. Moher, Modern Wireless Communications.
    Pearson Education, Inc., 2005.
  • S. Haykin, Adaptive Filter Theory.
    Prentice-Hall, 4th edition, 2002.
  • A. Paulraj, R. Nabar, and D. Gore, Introduction to Space-Time Wireless Communications.
    Cambridge University Press, 2003.
  • H. L. V. Trees, Optimum Array Processing.
    John Wiley & Sons, Inc., New York, NY, 2002.
  • M. Haardt, Efficient One-, Two-, and Multidimensional High-Resolution Array Signal Processing.
    Shaker Verlag GmbH, 1996, ISBN: 978-3-8265-2220-8.
  • G. Strang, Linear Algebra and Its Applications.
    Thomson Brooks/Cole Cengage learning.
  • G. Strang, Introduction to Linear Algebra.
    Wellesley - Cambridge Press, Fifth Edition.
  • L. L. Scharf, Statistical Signal Processing.
    Addison-Wesley Publishing Co., 1991.
  • S. M. Kay, Fundamentals of Statistical Signal Processing, Estimation Theory.
    Prentice-Hall, Englewood Cliffs, N.J., 1993.
  • M. Haardt, M. Pesavento, F. Roemer, and M. N. El Korso, Subspace methods and exploitation of special array structures.
    in Academic Press Library in Signal Processing: Volume 3 - Array and Statistical Signal Processing (A. M. Zoubir, M. Viberg, R. Chellappa, and S. Theodoridis, eds.), vol. 3, pp. 651 - 717, Elsevier Ltd., 2014, Chapter 15, ISBN 978-0-12-411597-2 ISBN: 978-3-8265-2220-8.

 

evaluation of teaching