Adaptive and array signal processing (tutoring)

I am looking for people who can teach me below topics.

1. 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 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

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 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

Beceriler: Matlab ve Mathematica , Matematik, Algoritma, İstatistik, Mühendislik

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( 0 değerlendirme ) Ilmenau, Germany

Proje NO: #31627931

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Hello I'm Mohamed saeed control engineer with high experience I have checked your jopb and ready to help you in your project.

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Hello, I have a PhD in signal processing and have been teaching "Adaptive filters" and "Array Signal Processing", at graduate level in the university. I can teach you all the above subjects from the beginning to the e Daha Fazla

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