Optimum filtering to improve the performance of DOA-ML estimators / Filtragem otima para melhorar o desempenho de estimadores DOA-ML

AUTOR(ES)
DATA DE PUBLICAÇÃO

2009

RESUMO

We approached the estimation of direction of arrival (DOA) of plane waves using an array of sensors. In the literature there are several DOA estimators, but we considered only the maximum likelihood (ML) estimators that generate candidates for DOA estimation and select the best one through an ML criterion. We also considered situations where the signal sources are spatially closely spaced and the signal-to-noise ratio is low. In these cases a performance degradation associated with the threshold effect occur. We demonstrated that we can improve the estimation performance by reducing the noise in the received data covariance matrix used to select the candidates. Then we proposed to modify the selection process using a new data covariance matrix, computed after an optimum multiband FIR filtering of the received data. We also proposed to modify the ML cost function to adapt it to the dimensions of the new covariance matrix and we considered 3 alternatives of modification. Some simulations showed that our proposal has better performance than known DOA methods, significantly reducing the threshold SNR

ASSUNTO(S)

processamento de sinais frequency discrimination estimation theory teoria da estimativa likelihood statistic digital filters (mathematics) verossimilhança (estatistica) signal processing filtros digitais (matematica) discriminadores de frequencia

ACESSO AO ARTIGO

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