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Multi-dimensional Capon spectral estimation using discrete Zhang neural networks

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Abstract

The minimum variance spectral estimator, also known as the Capon spectral estimator, is a high resolution spectral estimator used extensively in practice. In this paper, we derive a novel implementation of a very computationally demanding matched filter-bank based a spectral estimator, namely the multi-dimensional Capon spectral estimator. To avoid the direct computation of the inverse covariance matrix used to estimate the Capon spectrum which can be computationally very expensive, particularly when the dimension of the matrix is large, we propose to use the discrete Zhang neural network for the online covariance matrix inversion. The computational complexity of the proposed algorithm for one-dimensional (1-D), as well as for two-dimensional (2-D) and three-dimensional (3-D) data sequences is lower when a parallel implementation is used.

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Authors and Affiliations

  1. Département d’Électronique, Faculté des Sciences et de la Technologie et Sciences de la Matière, Université Kasdi Merbah, Ouargla, 30000, Algeria

    Abderrazak Benchabane & Fella Charif

  2. Département d’Electronique, Faculté des Sciences de l’ingénieur, Université Mentouri de Constantine, Constantine, 25000, Algeria

    Abdelhak Bennia

  3. Laboratoire LAMIH, FRE CNRS 3304 UVHC, Université de Valenciennes, Valenciennes, France

    Abdelmalik Taleb-Ahmed

Authors
  1. Abderrazak Benchabane
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  2. Abdelhak Bennia
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  3. Fella Charif
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  4. Abdelmalik Taleb-Ahmed
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Correspondence to Abderrazak Benchabane.

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Benchabane, A., Bennia, A., Charif, F. et al. Multi-dimensional Capon spectral estimation using discrete Zhang neural networks. Multidim Syst Sign Process 24, 583–598 (2013). https://doi.org/10.1007/s11045-012-0189-0

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  • DOI: https://doi.org/10.1007/s11045-012-0189-0

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