Multidimensional factorization through helical mapping

Abstract : This paper proposes a new perspective on the problem of multidimensional spectral factorization, through helical mapping: d-dimensional (dD) data arrays are vectorized, processed by 1D cepstral analysis and then remapped onto the original space. Partial differential equations (PDEs) are the basic framework to describe the evolution of physical phenomena. We observe that the minimum phase helical solution asymptotically converges to the dD semi-causal solution, and allows to decouple the two solutions arising from PDEs describing physical systems. We prove this equivalence in the theoretical framework of cepstral analysis, and we also illustrate the validity of helical factorization through a 2D wave propagation example and a 3D application to helioseismology.
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Contributeur : Francesca Raimondi <>
Soumis le : mercredi 9 mars 2016 - 13:54:39
Dernière modification le : jeudi 11 janvier 2018 - 06:26:33




Francesca Raimondi, Pierre Comon, Olivier Michel, Umberto Spagnolini. Multidimensional factorization through helical mapping. Signal Processing, Elsevier, 2016, 〈10.1016/j.sigpro.2016.08.014〉. 〈hal-01285533〉



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