Improving the threshold performance of maximum likelihood estimation of direction of arrival

  • Authors:

  • R. Krummenauer;M. Cazarotto;A. Lopes;P. Larzabal;P. Forster

  • Affiliations:

  • Laboratório DSPCOM, FEEC/6101, Universidade Estadual de Campinas - UNICAMP, 13083-852 Campinas-SP, Brazil and Departamento de Comunicaçíes, FEEC/6101, Universidade Estadual de Campi ...;Departamento de Comunicaçíes, FEEC/6101, Universidade Estadual de Campinas - UNICAMP, 13083-852 Campinas-SP, Brazil;Departamento de Comunicaçíes, FEEC/6101, Universidade Estadual de Campinas - UNICAMP, 13083-852 Campinas-SP, Brazil;Laboratoire SATIE, ENS de CACHAN, CNRS, Universud, 61 avenue du président Wilson, 94235 Cachan Cedex, France;Groupe d'ílectromagnétisme Appliqué (GEA), Université Paris X, 92410 Ville d'Avray, France

  • Venue:
  • Signal Processing
  • Year:
  • 2010

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Abstract

We propose to improve the performance of some direction of arrival (DOA) estimators using array of sensors. We consider those maximum likelihood (ML) estimators that generate some DOA candidates and select one of them through an ML criterion. Our proposal modifies the candidate selection process substituting the traditional sample covariance matrix by a new one computed after filtering the received data with an optimum noise reduction filter. Simulation results indicate an improvement of the performance at low signal-to-noise ratios (SNR) and a considerable reduction of the threshold SNR. The computation of the new selection cost function implies in a small increase in the overall computational effort.