Multidimensional factorization through helical mapping - Archive ouverte HAL
Article Dans Une Revue Signal Processing Année : 2017

Multidimensional factorization through helical mapping

Résumé

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.

Dates et versions

hal-01285533 , version 1 (09-03-2016)

Identifiants

Citer

Francesca Raimondi, Pierre Comon, Olivier J.J. Michel, Umberto Spagnolini. Multidimensional factorization through helical mapping. Signal Processing, 2017, 131, pp.171-180. ⟨10.1016/j.sigpro.2016.08.014⟩. ⟨hal-01285533⟩
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