Adaptive & Array Signal Processing
AASP
Prof. Dr.-Ing. João Paulo C. Lustosa da Costa
University of Brasília (UnB)
Department of Electrical Engineering (ENE)
Laboratory of Array Signal Processing
PO Box 4386
Zip Code 70.919-970, Brasília - DF
de Brasília
Homepage:Universidade
http://www.pgea.unb.br/~lasp
Laboratório de Processamento de Sinais em Arranjos
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Lectures in English
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
The lectures are taught in English
- to familiarize the students with the linguistic requirements of a
global economy, where technical discussions between internation
partners are usually conducted in English
- to allow the integration of international students at UnB, in our
case the student Stefanie Schwarz from TU Munich supported by
DAAD;
In many universities AASP subject is offered in English
- TU Ilmenau and TU Munich
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Information about the Lecturer

Academic Background
 Ph.D. in Electrical Engineering at TU Ilmenau in
Germany in 2010
 Master’s in Electrical Engineering at UnB in 2006
 Bachelor in Electronic Engineering at IME in 2003

Research areas
 Multidimensional Array Signal Processing
 MIMO Systems, parameter estimation, multilinear
algebra, principal component analysis

More information
 http://lattes.cnpq.br/1786889674911887
 http://www.pgea.unb.br/~lasp

Contact (to schedule meetings)
 joaopaulo.dacosta@ene.unb.br
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Research Area 1: Audio

Sound Sources Localization
Sound source 1
Sound source 2
Microphone array
 Applications: Intelligent Hearing Aid (PAI), interface between human and
machine, and data processing.
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Research Area 2: Telecommunications

Channel Modeling
Direction of Departure (DOD)
Transmitter Array: 1-D or 2-D
Direction of Arrival (DOA)
Receiver array: 1-D or 2-D
Delay
Frequency
Doppler shift
Time
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Information about the subject at
http://www.pgea.unb.br/~lasp
Login and password
thevenin
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Objectives of the subject

To allow the students to apply adaptive and array signal processing
schemes to solve problems in different scientific fields

The achievement of the objective is based on the final project and
on the exam.
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Bibliography
[1] http://www.pgea.unb.br/~lasp/
[2] S. Haykin, “Adaptive Filter Theory”, 3rd Edition, Prentice-Hall.
[3] A. H. Sayed, “Fundamentals of Adaptive Filtering”, John Wiley and Sons,
2003.
[4] M. Haardt, “Efficient One-, Two- and Multidimensional High-Resolution
Array Signal Processing”, Shaker, 1996.
[5] J. P. C. L. da Costa, “Parameter Estimation Techniques for MultiDimensional Array Signal Processing”, Shaker, 2010.
[6] S. Makino, T.-W. Lee, and S. Sawada, Blind Speech Separation, Springer,
2007.
[7] Slides, notes and papers suggested in this course.
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Grades

The final grade is given by:
 50 % of the AASP project;
 50 % of the written exam (probably at 29/11/2011).

Only if the student really deserves, an oral exam can be
provided to replace the grade of the written exam (probably at
13/12/2011).
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AASP Project
The title of the project should be informed until 25/08/2011. The
students that suggest a new research theme should include a short
description about the theme.
 only two students for each theme
 MATLAB should be used
 A preview presentation of the work at 08/09/2011
 The intermediate presentation at 27/10/2011 together with a short two
page abstract
 The best AASP projects will be submitted to WSA 2012: 30/10/2011
 The final presentation at 01/12/2011 and 06/12/2011 together with a four
page paper
• The abstract and the final written work should
 follow the IEEE template available at LASP homepage
 Include abstract, introduction, data model, technique description,
simulations, and conclusions

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Electric Circuits 2: Final Project



Lucas Fernandes Aguiar (Mechatronics)
- Coursed CE2/CEA in 2010.2
- His final work was transformed into a paper
- His paper has been accepted by the Solar World Congress (SWC)
that takes place in Kassel, Germany, from August 28 to September
02 2011
- Flight tickets paid by UnB
Flavio Augusto de Castro Junior (Networks)
- Coursed CE2/CEA in 2010.2 and now he is a REUNI scholarship
- His final work was transformed into a paper
- His paper has been accepted by the Simpósio Brasileiro de
Telecomunicações (SBrT) that takes place in Curitiba from 02 to 05
October 2011
- O evento poderia ter financiado as passagens e hospedagem
Both students were my research students in 2011.1.
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Theme on UAV field (1)
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Project title: Communication schemes for UAVs
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Description: In this work, the students have to research on the
literature the state-of-the-art communication schemes applied by
UAVs (Unmanned Aerial Vehicle) and also used in cooperative
MIMO communication. Depending on the complexity level, some
of these communication schemes can be simulated in MATLAB.

Difficulty level of the theory: High

Difficulty level of the programming: Medium

Tutor: João Paulo C. L. da Costa
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Theme on UAV field (2)
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Project title: Channel estimation for UAVs

Description: In this work, the students have to research on the
literature types of communication channels of UAVs. Moreover,
it will be important if the students could try to obtain
measurements for the channel and compare them to the
channels in the literature.

Difficulty level of the theory: Medium

Difficulty level of the programming: Medium

Tutor: João Paulo C. L. da Costa
Cotutor: Marco Marinho – PIBIC scholarship holder
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Theme on UAV field (3)
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Project title: Viability of Multiple Antennas in UAV for alignment
estimation
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Description: In this work, the students have to check the viability
of including antennas in different parts of an UAV in order to
estimate its alignment with respect to the base station. The
important points: if there is some electronic device for that in the
market. Furthermore, the cost, the size and the weight of such
device should be specified. The understanding of basic antenna
array concepts is important here as well as UAV concepts.

Difficulty level of the theory: Medium
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Difficulty level of the programming: Easy

Tutor: João Paulo C. L. da Costa
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Theme on UAV field (4)
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Project title: Estimation of Alignment of UAVs via Multiple
Antennas
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Description: In this work, the students have to consider a real
UAV and estimate its alignment by using multiple antenna
schemes. Several schemes in MATLAB are already available
and the student can use them.

Difficulty level of the theory: Medium

Difficulty level of the programming: Hard

Tutor: João Paulo C. L. da Costa
Cotutors: Luiz Fernando and Herlandson
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Theme on UAV field (5)
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Project title: Filtering of magnetometer signals to acquire a
better alignment estimation
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Description: In this work, the students will analyze the signals of
a magnetometer and will to propose a filter in order to have the
signals with less noise.

Difficulty level of the theory: Medium

Difficulty level of the programming: Medium

Tutor: João Paulo C. L. da Costa
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Theme on Microphone Array (1)
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Project title: Understanding the TRINICON algorithm
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Description: In this work, the students have to understand how
the TRINICON algorithms works and try to implement it in
MATLAB.
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Difficulty level of the theory: High

Difficulty level of the programming: High

Tutor: João Paulo C. L. da Costa
Cotutor: Rubens Martins
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Theme on Microphone Array (2)
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Project title: Finding the
microphone array signals
multi-dimensional
structure
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Description: In this work, the students have to search for a multidimensional structure of microphone array signals. There is
already some code developed in MATLAB.

Difficulty level of the theory: High

Difficulty level of the programming: High

Tutor: João Paulo C. L. da Costa
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Theme on generation of multidimensional models
and of metadata (1)
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Project title: Application of signal processing schemes for fast
schemes for knowledge
formation and generation of
multidimensional models and of metadata
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Description: In this work, the students have to apply signal
processing schemes to detect data corruption. Before starting
working a bibliographical review is necessary.

Difficulty level of the theory: Hard

Difficulty level of the programming: Medium

Tutor: João Paulo C. L. da Costa
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Theme on Sniper detector (1)
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Sniper detection
 green star: sniper
 yellow circle: phone array
 red crossed circle: target
 red line: muzzleblast
Problem
 Short signal in time
 Schockwave effect
Description: In this work, the
students have to apply signal
processing schemes to filter
spatially the sound signals. Before
starting working a bibliographical
review is necessary.
Difficulty level of the theory: Hard
Difficulty level of the programming:
Medium
Tutor: João Paulo C. L. da Costa
Gunshot audio signals geometry
Position (m)

Position (m)
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Theme on Intelligent Hearing aid (1)

Intelligent Hearing aid
 allows the selection of the desired
sound source;
 filters spatially the desired sound
source(s).




Description: In this work, the
students have to apply signal
processing schemes to filter
spatially the sound signals. Before
starting working a bibliographical
review is necessary.
Difficulty level of the theory: Hard
Difficulty level of the programming:
Medium
Tutor: João Paulo C. L. da Costa
 Example: using a traditional hearing aid
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Intelligent Hearing aid (1)

Intelligent Hearing aid
 allows the selection of the desired
sound source;
 filters spatially the desired sound
source(s).




Description: In this work, the
students have to apply signal
processing schemes to filter
spatially the sound signals. Before
starting working a bibliographical
review is necessary.
Difficulty level of the theory: Hard
Difficulty level of the programming:
Medium
Tutor: João Paulo C. L. da Costa
 Example: using an intelligent hearing aid
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Environment Monitoring (1)
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Environment monitoring
 Each sensor node with one or more microphones
Description: In this work, the
students have to apply
signal processing schemes
to filter spatially the sound
signals of birds. Before
starting
working
a
bibliographical review is
necessary.
Difficulty level of the theory:
Hard
Difficulty
level
of
the
programming: Medium
Tutor: João Paulo C. L. da
Costa
Cotutor:Alexandre (Biology)
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Parameter Estimation of MIMO scenarios (1)
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Project title: To estimate the parameters of IlmProp scenarios
applying multidimensional parameter estimation schemes
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Description: In this work, the students will use the code already
developed in MATLAB to estimate the parameter of scenarios
generated using the IlmProp.

Difficulty level of the theory: High

Difficulty level of the programming: High

Tutor: João Paulo C. L. da Costa
Group: Stefanie Schwarz and Marco
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Wind Tunnel Evaluation (1)

Wind tunnel evaluation
Array
Source: Carine El Kassis [4].
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Motivation (1)

An unlimited list of applications
 Radar;
 Sonar;
 Communications;
 Medical imaging;
 Chemistry;
 Food industry;
 Pharmacy;
 Psychometrics;
 Reflection seismology;
 EEG;
…
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Motivation (2)

Stock Markets: One example of [1]
 Information: Long Term Government Bond interest rates.
Canada, USA, 6 European countries and Japan.
 Result: by visual inspection of the Eigenvalues (EVD).
Three main components: Europe, Asia and North America.
[1]: M. Loteran, “Generating market risk scenarios using principal components analysis: methodological and
practical considerations”, in the Federal Reserve Board, March, 1997.
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Motivation (3)
Ultraviolet-visible (UV-vis) Spectrometry [2]
Wavelength

Oxidation state
Radiation
Non-identified substance
samples
 Result: successful application of tensor calculus.
In [2], the model order is estimated via the core consistency
analysis (CORCONDIA) by visual inspection.
[2]: K. S. Von Age, R. Bro, and P. Geladi, “Multi-way analysis with applications in the chemical sciences,”
Wiley, Aug. 2004.
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Exam

Preparation based on
 slides;
 Book
• S. Haykin, “Adaptive Filter Theory”, 3rd Edition, PrenticeHall
• Solve the recommended exercicises
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Content of AASP (1)
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
1 Introduction
- 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
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Content of AASP (2)
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- 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
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Content of AASP (3)
- 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
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Content of AASP (4)
3.5 Least Mean Square (LMS) 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
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Content of AASP (5)
- 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
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Content of AASP (6)
4.10 Direction of Arrival Estimation with Hexagonal ESPAR
Arrays
5 Tensor-Based Signal Processing
5.1 Higher Order Singular Value Decomposition
5.2 Parallel Factor Analysis (PARAFAC)
5.3 Closed-Form PARAFAC
5.4 Examples of multidimensional extensions of matrix
based schemes
6 Maximum Likelihood Estimators
6.1 Maximum Likelihood Principle
6.2 The Fisher Information Matrix and the Cramer Rao
Lower Bound (CRLB)
- Efficiency
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Content of AASP (7)
- CRLB for 1-D direction finding applications
- Asymptotic CRLB
7 Audio Signal Processing
7.1 TRIple-N Independent component analysis for convolutive
mixtures (TRINICON)
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