Estimacao do numero de fontes via norma L0,55

• Ano de defesa: 2016
• Autor(a) principal: Floriano, Giovanni Henrique Faria
• Orientador(a): Guimar??es, Dayan Adionel , Souza, Rausley Adriano Amaral de
• Banca de defesa: Guimar??es, Dayan Adionel , Souza, Rausley Adriano Amaral de , Pimenta, Tales Cleber , Chaves, Felipe Emanoel
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Instituto Nacional de Telecomunica????es
• Programa de Pós-Graduação: Mestrado em Engenharia de Telecomunica????es
• Departamento: Instituto Nacional de Telecomunica????es
• País: Brasil
• Palavras-chave em Português: Numero de fontes de sinais; AIC - akaike information criterion; MDF - minimum description lenght; Matriz aleatoria; Metodo baseado em norma; Engenharia de telecomunicacoes
• Palavras-chave em Inglês: Estimation of the number of sources; Akaike information criterion (AIC); method of minimum description length (MDL); theory of random matrix (RMT); method norm-based (NB).
• Área do conhecimento CNPq: Engenharia - Telecomunica????es
• Link de acesso: http://tede.inatel.br:8080/tede/handle/tede/4

In 2014, an empirical method was proposed to estimate the number of signal sources being impinged by multiple sensors, named norm-based algorithm (NB). This method is based on analysis of the Euclidean norm of vectors whose elements are the eigenvalues, standardized and scaled nonlinearly, from the received signal covariance matrix and the also standardized corresponding indexes. These norms are then used to discriminate the largest eigenvalues among all of them, allowing the estimation of the number of sources. In this dissertation we propose the improved norm-based algorithm (iNB) which uses the ???0,55-norm to discriminate the largest eigenvalues. Unlike the NB algorithm, iNB does not use the nonlinear scaling, and also does not require the definition of an empirical constant to perform the discrimination of the eigenvalues. To evaluate the effectiveness of this proposal (iNB) their results were compared with the results obtained using estimators based on Akaike information criterion (AIC), the method length minimum description (MLD) and random matrix theory (RMT). These comparisons show that iNB algorithm can overcome the performance of one or more of these estimators in various situations and that iNB always exceeds the NB Algorithm.