Universidade de Brasília Laboratório de Processamento de Sinais em Arranjos 1 Adaptive & Array Signal Processing AASP Prof. Dr.-Ing. João Paulo C. Lustosa.

Apresentação em tema: "Universidade de Brasília Laboratório de Processamento de Sinais em Arranjos 1 Adaptive & Array Signal Processing AASP Prof. Dr.-Ing. João Paulo C. Lustosa."— Transcrição da apresentação:

1 Universidade de Brasília Laboratório de Processamento de Sinais em Arranjos 1 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 Homepage: http://www.pgea.unb.br/~lasp

2 Universidade de Brasília Laboratório de Processamento de Sinais em Arranjos 2 Lectures in English   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

3 Universidade de Brasília Laboratório de Processamento de Sinais em Arranjos 3 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

4 Universidade de Brasília Laboratório de Processamento de Sinais em Arranjos 4 Research Area 1: Audio  Sound Sources Localization Microphone array Sound source 1 Sound source 2   Applications: Intelligent Hearing Aid (PAI), interface between human and machine, and data processing.

5 Universidade de Brasília Laboratório de Processamento de Sinais em Arranjos 5 Receiver array: 1-D or 2-D Frequency Time Transmitter Array: 1-D or 2-D Direction of Arrival (DOA) Delay Doppler shift Direction of Departure (DOD) Research Area 2: Telecommunications   Channel Modeling

6 Universidade de Brasília Laboratório de Processamento de Sinais em Arranjos 6 Information about the subject at Information about the subject at http://www.pgea.unb.br/~lasp  Login and password thevenin thevenin

7 Universidade de Brasília Laboratório de Processamento de Sinais em Arranjos 7 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.

8 Universidade de Brasília Laboratório de Processamento de Sinais em Arranjos 8 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 Multi- Dimensional 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.

9 Universidade de Brasília Laboratório de Processamento de Sinais em Arranjos 9 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).

10 Universidade de Brasília Laboratório de Processamento de Sinais em Arranjos 10 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

11 Universidade de Brasília Laboratório de Processamento de Sinais em Arranjos 11 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.

12 Universidade de Brasília Laboratório de Processamento de Sinais em Arranjos 12 Theme on UAV field (1)   Project title: Communication schemes for UAVs   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

13 Universidade de Brasília Laboratório de Processamento de Sinais em Arranjos 13 Theme on UAV field (2)   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

14 Universidade de Brasília Laboratório de Processamento de Sinais em Arranjos 14 Theme on UAV field (3)   Project title: Viability of Multiple Antennas in UAV for alignment estimation   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   Difficulty level of the programming: Easy   Tutor: João Paulo C. L. da Costa

15 Universidade de Brasília Laboratório de Processamento de Sinais em Arranjos 15 Theme on UAV field (4)   Project title: Estimation of Alignment of UAVs via Multiple Antennas   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

16 Universidade de Brasília Laboratório de Processamento de Sinais em Arranjos 16 Theme on UAV field (5)   Project title: Filtering of magnetometer signals to acquire a better alignment estimation   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

17 Universidade de Brasília Laboratório de Processamento de Sinais em Arranjos 17 Theme on Microphone Array (1)   Project title: Understanding the TRINICON algorithm   Description: In this work, the students have to understand how the TRINICON algorithms works and try to implement it in MATLAB.   Difficulty level of the theory: High   Difficulty level of the programming: High   Tutor: João Paulo C. L. da Costa Cotutor: Rubens Martins

18 Universidade de Brasília Laboratório de Processamento de Sinais em Arranjos 18 Theme on Microphone Array (2)   Project title: Finding the multi-dimensional structure of microphone array signals   Description: In this work, the students have to search for a multi- dimensional 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

19 Universidade de Brasília Laboratório de Processamento de Sinais em Arranjos 19 Theme on generation of multidimensional models and of metadata (1)   Project title: Application of signal processing schemes for fast schemes for knowledge formation and generation of multidimensional models and of metadata   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

20 Universidade de Brasília Laboratório de Processamento de Sinais em Arranjos Theme onSniper detector (1) Theme on Sniper detector (1)  Sniper detection Gunshot audio signals geometry Position (m)   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

21 Universidade de Brasília Laboratório de Processamento de Sinais em Arranjos Theme onIntelligent Hearing aid (1) Theme on Intelligent Hearing aid (1)  Intelligent Hearing aid   allows the selection of the desired sound source;   filters spatially the desired sound source(s).   Example: using a traditional hearing aid   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

22 Universidade de Brasília Laboratório de Processamento de Sinais em Arranjos Intelligent Hearing aid (1)  Intelligent Hearing aid   allows the selection of the desired sound source;   filters spatially the desired sound source(s).   Example: using an intelligent hearing aid   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

23 Universidade de Brasília Laboratório de Processamento de Sinais em Arranjos Environment Monitoring (1)  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)

24 Universidade de Brasília Laboratório de Processamento de Sinais em Arranjos 24 Parameter Estimation of MIMO scenarios (1)   Project title: To estimate the parameters of IlmProp scenarios applying multidimensional parameter estimation schemes   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

25 Universidade de Brasília Laboratório de Processamento de Sinais em Arranjos Wind Tunnel Evaluation (1)  Wind tunnel evaluation Wind Array Source: Carine El Kassis [4].

26 Universidade de Brasília Laboratório de Processamento de Sinais em Arranjos Motivation (1)  An unlimited list of applications  Radar;  Sonar;  Communications;  Medical imaging;  Chemistry;  Food industry;  Pharmacy;  Psychometrics;  Reflection seismology;  EEG;  …

27 Universidade de Brasília Laboratório de Processamento de Sinais em Arranjos Motivation (2)  Stock Markets: One example of [1] [1]: M. Loteran, “Generating market risk scenarios using principal components analysis: methodological and practical considerations”, in the Federal Reserve Board, March, 1997.   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.

28 Universidade de Brasília Laboratório de Processamento de Sinais em Arranjos Motivation (3)  Ultraviolet-visible (UV-vis) Spectrometry [2] [2]: K. S. Von Age, R. Bro, and P. Geladi, “Multi-way analysis with applications in the chemical sciences,” Wiley, Aug. 2004. Non-identified substance Radiation Wavelength Oxidation state pH samples   Result: successful application of tensor calculus. In [2], the model order is estimated via the core consistency analysis (CORCONDIA) by visual inspection.

29 Universidade de Brasília Laboratório de Processamento de Sinais em Arranjos 29 Exam   Preparation based on   slides;   Book S. Haykin, “Adaptive Filter Theory”, 3rd Edition, Prentice- Hall Solve the recommended exercicises

30 Universidade de Brasília Laboratório de Processamento de Sinais em Arranjos Content of AASP (1)   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

31 Universidade de Brasília Laboratório de Processamento de Sinais em Arranjos 31 Content of AASP (2) - 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

32 Universidade de Brasília Laboratório de Processamento de Sinais em Arranjos 32 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

33 Universidade de Brasília Laboratório de Processamento de Sinais em Arranjos 33 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

34 Universidade de Brasília Laboratório de Processamento de Sinais em Arranjos 34 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

35 Universidade de Brasília Laboratório de Processamento de Sinais em Arranjos 35 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

36 Universidade de Brasília Laboratório de Processamento de Sinais em Arranjos 36 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)