Adaptive and Array Signal Processing, Complete - Interactive curriculae of TU Ilmenau
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| module properties Adaptive and Array Signal Processing, Complete in degree program Master Medientechnologie 2017 | |
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| module number | 200484 |
| examination number | 2100807 |
| department | Department of Electrical Engineering and Information Technology |
| ID of group | 2111 (Communications Engineering) |
| module leader | Prof. Dr. Martin Haardt |
| term | winter term only |
| language | Englisch |
| credit points | 10 |
| on-campus program (h) | 90 |
| self-study (h) | 210 |
| obligation | elective module |
| exam | written examination performance, 150 minutes |
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| link to Moodle course | |
| teacher | Prof. Dr. Haardt, Martin |
| signup details for alternative examinations | |
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| 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 2.3 Linear 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 4.5 Forward-backward averaging 4.6 Real-valued subspace estimation 4.7 1-D Unitary ESPRIT - Reliability test - Applications in Audio Coding 4.8 Multidimensional Extensions - 2-D MUSIC - 2-D Unitary ESPRIT - R-D Unitary ESPRIT 4.9 Multidimensional Real-Time Channel Sounding 4.10 Direction of Arrival Estimation with Hexagonal ESPAR Arrays 5 Tensor-Based Signal Processing and Machine Learning 5.1 Introduction and Motivation 5.2 Fundamental Concepts of Tensor Algebra 5.3 Elementary Tensor Decompositions - Higher Order SVD (HOSVD) - CANDECOMP / PARAFAC (CP) Decomposition 5.4 Tensors in Selected Signal Processing and Deep Neural Network Applications 6 Maximum Likelihood Estimators 6.1 Maximum Likelihood Principle 6.2 The Fisher Information Matrix and the Cramer Rao Lower Bound (CRLB) - Efficiency - CRLB for 1-D direction finding applications - Asymptotic CRLB |
| media of instruction and technical requirements for education and examination in case of online participation | Skript, Overheadprojektor, Beamer Script, projector |
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