Algorithms for estimation and classification based on sensor arrays
Adaptive algorithms, combinations and applications in deconvolution
Application of state estimation and machine learning techniques for the energy man...
Grant number: | 17/22226-1 |
Support Opportunities: | Scholarships in Brazil - Master |
Start date until: | June 01, 2018 |
End date until: | July 31, 2020 |
Field of knowledge: | Engineering - Electrical Engineering - Telecommunications |
Agreement: | Coordination of Improvement of Higher Education Personnel (CAPES) |
Principal Investigator: | Vitor Heloiz Nascimento |
Grantee: | Paulo Otávio Moreira dos Santos |
Host Institution: | Escola Politécnica (EP). Universidade de São Paulo (USP). São Paulo , SP, Brazil |
Abstract The acoustic imaging problem consists of mapping the directions and intensities of sound sources using a microphone array.These maps are used, e.g., to design airplanes, cars, and trains that are quieter and more aerodynamically efficient, and also to analyze structures such as concert halls and turbines. For arrays with a large number of microphones and images with high resolution, the number of computations needed for the estimation of an acoustic image is high. Recently, this research group developed several methods for reducing this computational cost, in particular the Kronecker array transform (KAT), which in certain conditions allows a reduction of one order of magnitude in the number of computations, and methods based on the non-equispaced fast Fourier transform (NFFT).In this work we intend to compare the algorithms based on the KAT and NFFT with other methods available in the literature, developed for beamforming. We also intend to extend these other methods to use in the estimation of acoustic images.A further goal of this work is related to the most advanced methods currently available for acoustic image estimation, which are based on the solution of certain regularized optimization problems. We intend to apply new low-cost options for sparse optimization to the acoustic estimation problem, and to analyze their performance in terms of computational cost and resolution. (AU) | |
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