Submitted by Automa??o e Estat?stica (sst@bczm.ufrn.br) on 2017-03-09T22:35:46Z No. of bitstreams: 1 EbertonDaSilvaMarinho_TESE.pdf: 27289302 bytes, checksum: afbb77aed251ffa31c13cdae992f063a (MD5) / Approved for entry into archive by Arlan Eloi Leite Silva (eloihistoriador@yahoo.com.br) on 2017-03-13T21:12:47Z (GMT) No. of bitstreams: 1 EbertonDaSilvaMarinho_TESE.pdf: 27289302 bytes, checksum: afbb77aed251ffa31c13cdae992f063a (MD5) / Made available in DSpace on 2017-03-13T21:12:47Z (GMT). No. of bitstreams: 1 EbertonDaSilvaMarinho_TESE.pdf: 27289302 bytes, checksum: afbb77aed251ffa31c13cdae992f063a (MD5) Previous issue date: 2016-08-22 / O Compressive Sensing (CS) ? uma t?cnica de processamento de dados eficiente na recupera??o e constru??o de sinais a partir de uma taxa de amostragem menor que a requerida pelo teorema de Shannon-Nyquist. Esta t?cnica permite uma grande redu??o de dados para sinais que podem ser esparsamente representados. A Transformada Wavelet tem sido utilizada para comprimir e representar muitos sinais naturais, incluindo s?smicos, de uma forma esparsa. H? diversos algoritmos de reconstru??o de sinais que utilizam a t?cnica de CS, como por exemplo: o $\ell_1$-MAGIC, o Fast Bayesian Compressive Sensing (Fast BCS) e o Stagewise Orthogonal Matching Pursuit (StOMP). Esta tese compara a recupera??o de tra?os s?smicos sob uma perspectiva estat?stica usando diferentes m?todos do CS, transformadas wavelets e taxas de amostragens. Mediu-se a correla??o entre o Erro Relativo (ER) de recupera??o pelo CS e as medi??es: coeficiente de varia??o, assimetria, curtose e entropia do sinal original. Parece haver uma correla??o entre a curtose e entropia do sinal com o ER de reconstru??o pelo CS. Ademais, foi analizado a distribui??o do ER no CS. O $\ell_1$-MAGIC teve melhores resultados para taxas de amostragens at? 40%. Al?m disso, a distribui??o do ER no $\ell_1$-MAGIC teve mais histogramas normais, sim?tricos e mesoc?rticos que no Fast BCS. Entretanto, para taxas de amostragem acima de 50%, o Fast BCS mostrou um melhor desempenho em rela??o ? m?dia do ER.

In this dissertation, sparse processing of signals for directions-of-arrival (DoAs) estimation is addressed in the framework of Compressive Sensing (CS). In particular, DoAs estimation problem for different types of sources, systems, and applications are formulated in the CS paradigm. In addition, the fundamental conditions related to the ``Sparsity'' and ``Linearity'' are carefully exploited in order to apply confidently the CS-based methodologies. Moreover, innovative strategies for various systems and applications are developed, validated numerically, and analyzed extensively for different scenarios including signal to noise ratio (SNR), mutual coupling, and polarization loss. The more realistic data from electromagnetic (EM) simulators are often considered for various analysis to validate the potentialities of the proposed approaches. The performances of the proposed estimators are analyzed in terms of standard root-mean-square error (RMSE) with respect to different degrees-of-freedom (DoFs) of DoAs estimation problem including number of elements, number of signals, and signal properties. The outcomes reported in this thesis suggest that the proposed estimators are computationally efficient (i.e., appropriate for real time estimations), robust (i.e., appropriate for different heterogeneous scenarios), and versatile (i.e., easily adaptable for different systems).